What should I focus on for a data engineer interview?

What makes these interviews different?

Pipelines and data modeling stand out. These topics appeared more often in data engineer interviews than across all interviews. Data quality and performance tuning also appeared more often. Most questions were at mid-level or senior level.

Start by checking whether you can explain how data moves and how it is stored:

  • Can you walk through ingestion, transformations, and orchestration in an end-to-end pipeline?
  • Can you explain how an incremental load works, and compare batch processing with streaming?
  • Can you state what each row in a fact table represents?
  • Can you explain when to use slowly changing dimensions, or choose a star schema over a snowflake schema?

Which SQL answers should I check first?

Join types were the most repeated question, asked in 8 of 180 interviews. Other common questions covered WHERE versus HAVING, UNION versus UNION ALL, and common table expressions.

Check which rows each join keeps. Reviewed answers missed that a full outer join returns matching rows plus unmatched rows from both tables, with NULLs on the missing side. Can you explain that result clearly?

For the other common questions, can you explain the difference or use case, not just define the terms? Can you say when you would use a window function?

What code should I be able to write?

Coding tasks appeared in 52 of 180 interviews. Test yourself on the recorded tasks:

  • Write SQL to find the second-highest salary and the highest-paid employee in each department.
  • Reverse a string, remove duplicate list items, and implement binary search in Python.

For Python decorators, check that you can show the implementation pattern. Reviewed answers missed accepting a function, returning an inner wrapper, and applying it with @decorator.

How would I check data and handle a pipeline problem?

The questions cover validation, reconciliation, incident diagnosis, and recovery. Can you explain how you would compare pipeline output with source data? What would you check to find where a problem started? Can you describe how you would recover from it?

Can I explain platform choices and my own work?

For Snowflake, can you explain the roles of storage, compute, and cloud services? Can you compare storage formats and discuss cost trade-offs?

For project questions, be clear about what you owned. What was a stakeholder disagreement about? How did you set delivery priorities? During an incident, what did you communicate?

What Data Engineer interviews cover

ETL/ELT & data pipelines71% of interviews
Data modeling & warehousing48% of interviews
Data quality, validation & deduplication41% of interviews
Performance optimization & profiling41% of interviews
Relational databases38% of interviews
SQL36% of interviews

The 20 most asked Data Engineer questions

Counted by interview, not by mention: a question asked twice in one conversation counts once.

  1. What are the different SQL join types, what rows do they return, and when would you use each?

    Asked in 8 of 180 interviews · last asked Oct 7 · Concept · Junior

    Show answer

    INNER JOIN returns matching rows only. LEFT and RIGHT JOIN keep every row from the left or right table, adding NULLs for missing matches. FULL OUTER JOIN returns matches plus unmatched rows from both tables, with NULLs on the missing side. CROSS JOIN returns every possible pair of rows.

    • Use INNER for matched records; LEFT or RIGHT when all rows from one table must stay.
    • Use FULL OUTER to compare datasets and find missing records on either side; database support varies.
    • Use CROSS JOIN to create all combinations, such as every product paired with every store.
    • A self join joins a table to itself, such as employees to managers; aliases distinguish each copy.

    What the interviewer checks: The interviewer checks whether you can choose a join based on which rows must appear in the result.

    Common mistake (in 4 of 7 reviewed answers): Many answers forget that FULL OUTER JOIN keeps unmatched rows from both tables, with NULLs on the missing side.

    Other ways it was asked
    • “What is the difference between an inner join and a left join?”
    • “What is the difference between an outer join, a left join, and an inner join?”
    • “What are the different types of SQL joins, and how do they work?”

  2. What's the difference between WHERE and HAVING?

    Asked in 7 of 180 interviews · last asked Oct 7 · Concept · Junior

    Show answer

    WHERE filters individual rows before grouping and aggregation. HAVING filters groups after aggregation. Use WHERE for row conditions, such as order status, and HAVING for group conditions, such as COUNT(*) > 5.

    • WHERE controls which rows enter the aggregation.
    • HAVING controls which groups appear in the result.
    • Aggregate functions cannot go directly in WHERE at the same query level.
    • A query can use both WHERE and HAVING.
    SQL
    SELECT customer_id, COUNT(*) AS completed_orders
FROM orders
WHERE status = 'completed'
GROUP BY customer_id
HAVING COUNT(*) > 5;

    What the interviewer checks: The interviewer is checking whether you understand row filtering versus group filtering and when aggregation happens.

    Other ways it was asked
    • “What is the difference between WHERE and HAVING?”
    • “What is the difference between WHERE and HAVING, and when would you use each?”
    • “What is the difference between the WHERE and HAVING clauses in SQL?”

  3. How do UNION and UNION ALL differ?

    Asked in 7 of 180 interviews · last asked Oct 7 · Concept · Junior

    Show answer

    UNION combines query results and removes duplicate rows. UNION ALL combines results and keeps duplicates. Use UNION when you need distinct rows, and UNION ALL when duplicates are acceptable. UNION ALL is usually faster because it skips duplicate removal.

    • UNION removes duplicates based on the whole selected row, not just one column.
    • Both require the same number of columns, with compatible types in matching positions.
    • Neither guarantees row order without a final ORDER BY.

    What the interviewer checks: The interviewer checks whether you understand duplicate handling, performance, and when to use each operator.

    Other ways it was asked
    • “What is the difference between UNION and UNION ALL?”
    • “What is the difference between UNION and UNION ALL in SQL?”
    • “Explain the difference between UNION and UNION ALL. When would you use each?”

  4. What is a common table expression, and when would you use one?

    Asked in 7 of 180 interviews · last asked Sep 25 · Concept · Junior

    Show answer

    A common table expression (CTE) is a named query result defined with WITH and used within one SQL statement. I use CTEs to break complex queries into clear steps or replace nested subqueries. Recursive CTEs can handle hierarchies, such as employee reporting lines.

    • A CTE exists only within its SQL statement; it is not a stored table.
    • The statement can reference a CTE by name, often more than once.
    • A CTE does not guarantee faster execution; optimization and materialization depend on the database and version.
    SQL
    WITH customer_totals AS (
    SELECT customer_id, SUM(amount) AS total_amount
    FROM orders
    GROUP BY customer_id
)
SELECT customer_id, total_amount
FROM customer_totals
WHERE total_amount > 1000;

    What the interviewer checks: The interviewer checks whether you understand CTE scope and can use CTEs to make SQL easier to read.

    Other ways it was asked
    • “What are common table expressions (CTEs), and how can they be used instead of subqueries?”
    • “What is the basic syntax for defining a common table expression (CTE) in SQL?”
    • “What are common table expressions in SQL, and when would you use them?”

  5. What are slowly changing dimensions, and when would you use them?

    Asked in 7 of 180 interviews · last asked Oct 7 · Concept · Mid-level

    Show answer

    Slowly changing dimensions (SCDs) are warehouse dimensions whose attributes change over time, such as a customer's address. I use SCD methods to control whether reports show current values or past values. Type 1 overwrites old values. Type 2 keeps history by adding a new row for each change.

    • Use Type 1 when old values are not needed, such as fixing a spelling error.
    • Use Type 2 when reports must reflect the values that applied when an event happened.
    • Type 2 closes the previous version and adds a row with effective dates and a current flag.
    • Each Type 2 version has a unique surrogate key; facts reference the version valid for the event.

    What the interviewer checks: The interviewer checks whether you can choose between overwriting values and keeping history, and explain Type 2 implementation.

    Other ways it was asked
    • “How do you implement Slowly Changing Dimension Type 2 (SCD Type 2) in a data warehouse?”
    • “What are slowly changing dimensions (SCDs) in data warehousing?”
    • “What is a slowly changing dimension (SCD) in data warehousing?”

  6. What are SQL window functions, and when would you use one?

    Asked in 6 of 180 interviews · last asked Oct 7 · Concept · Mid-level

    Show answer

    SQL window functions calculate values across related rows without combining them into one row, unlike GROUP BY. They use an OVER clause to define the window. I use them for rankings, running totals, moving averages, and comparing a row with the previous or next row.

    • PARTITION BY splits rows into groups for the calculation; it does not reduce the row count.
    • ORDER BY inside OVER sets the calculation order, not the final output order.
    • Common functions include ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, and aggregates such as SUM.
    • For running totals or moving averages, set an explicit window frame to control which rows are included.
    SQL
    SELECT
  account_id,
  transaction_id,
  amount,
  SUM(amount) OVER (
    PARTITION BY account_id
    ORDER BY transaction_id
    ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
  ) AS running_total
FROM transactions;

    What the interviewer checks: The interviewer checks whether you can calculate across rows while keeping row-level detail and choose the right window settings.

    Other ways it was asked
    • “What are SQL window functions, and when would you use them?”
    • “What types of SQL window functions do you know?”

  7. When would you use a star schema versus a snowflake schema?

    Asked in 6 of 180 interviews · last asked Sep 22 · Concept · Mid-level

    Show answer

    I use a star schema for simple reporting, with a central fact table linked to flat dimension tables. I use a snowflake schema when splitting dimensions into related tables helps manage shared data or hierarchies. Star schemas need fewer joins; snowflake schemas reduce repeated dimension data but make queries more complex.

    • Fact tables store measurements; dimension tables describe them.
    • Star schemas are easier for analysts and reporting tools to use.
    • Snowflake schemas normalize dimensions, reducing duplication and adding joins.
    • Query speed depends on the database and workload, not just the schema.

    What the interviewer checks: The interviewer is checking whether you can balance reporting simplicity, data duplication, and query complexity.

    Other ways it was asked
    • “When would you use a star schema or a snowflake schema for data modeling?”
    • “What is the difference between star and snowflake schemas, and when would you use each?”
    • “What is the difference between a snowflake schema and a star schema?”

  8. What are decorators in Python?

    Asked in 5 of 180 interviews · last asked Oct 7 · Concept · Junior

    Show answer

    A Python decorator is a callable that modifies or replaces a function or class without changing its source code. A typical function decorator accepts a function and returns an inner wrapper that adds behavior. I use decorators for logging, timing, caching, or access checks.

    • Apply a decorator with @decorator above the function definition.
    • @decorator means the function is replaced by decorator(function) when it is defined.
    • Wrappers commonly use *args and **kwargs to pass arguments through and return the original result.
    • Use functools.wraps to preserve metadata such as the original function’s name and documentation.
    Python
    from functools import wraps
def log_calls(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        print(f"Calling {func.__name__}")
        return func(*args, **kwargs)
    return wrapper
@log_calls
def add(a, b):
    return a + b

    What the interviewer checks: The interviewer checks whether you understand how decorators wrap functions and add reusable behavior.

    Common mistake (in 3 of 5 reviewed answers): Many answers forget that a typical decorator accepts a function, returns an inner wrapper, and is applied with @decorator.

    Other ways it was asked
    • “What are Python decorators, and when would you use them?”
    • “How do you create and use a decorator in Python?”
    • “What is a decorator in Python?”

  9. Explain Snowflake's architecture and the roles of its storage, compute, and cloud services layers.

    Asked in 5 of 180 interviews · last asked Sep 20 · Concept · Mid-level

    Show answer

    Snowflake separates storage, compute, and cloud services into three layers. Storage keeps table data in compressed, column-based micro-partitions in cloud object storage. Compute uses virtual warehouses to run queries and load data, with resources that scale independently of storage. Cloud services coordinate the system, handling authentication, access control, metadata, query optimization, and transactions.

    • Separate virtual warehouses can query the same stored data without sharing compute resources.
    • Storage persists when warehouses stop; warehouses can be suspended when not needed.
    • Cloud services manage metadata and coordination, not the bulk storage of table data.

    What the interviewer checks: The interviewer checks whether you understand each layer’s role and how separating storage from compute supports independent scaling.

    Other ways it was asked
    • “Can you explain Snowflake’s architecture?”
    • “How is Snowflake's architecture organized?”
    • “How do the cloud services and storage layers work in Snowflake?”

  10. What is Snowflake?

    Asked in 5 of 180 interviews · last asked Sep 30 · Concept · Junior

    Show answer

    Snowflake is a managed cloud data platform used for data warehousing and analytics. It separates storage from compute, so teams can scale query resources without moving data. Organizations choose it for low maintenance, support for structured and semi-structured data, and secure data sharing.

    • Runs on AWS, Azure, and Google Cloud.
    • Independent compute clusters, called virtual warehouses, help keep workloads from competing for resources.
    • Snowflake manages infrastructure; teams still manage data access, query performance, and spending.
    • Storage and compute are billed separately; auto-suspend helps control idle compute costs.

    What the interviewer checks: The interviewer is checking whether you understand Snowflake’s purpose, architecture, and main benefits.

    Other ways it was asked
    • “What are the essential features of Snowflake?”
    • “What is Snowflake, and why might an organization choose it as a cloud data warehouse?”

  11. What is the difference between ETL and ELT, and when would you use each in data pipeline development?

    Asked in 5 of 180 interviews · last asked Sep 30 · Concept · Mid-level

    Show answer

    ETL means extract, transform, load: data is changed before it enters the target system. ELT means extract, load, transform: data is loaded first, then changed inside the target system. I use ETL when data must be cleaned or masked before loading, or the target has limited processing power. I use ELT when the target can efficiently process large datasets.

    • ETL uses processing outside the target; ELT uses the target’s processing power.
    • ELT can keep raw data for new transformations, but needs access controls and storage.
    • Choose based on privacy rules, data volume, processing costs, and target capabilities.

    What the interviewer checks: The interviewer checks whether you understand where transformation happens and can choose based on system needs.

    Other ways it was asked
    • “What is the difference between ETL and ELT?”
    • “What are ETL and ELT, and how do they differ?”

  12. What is the difference between repartition and coalesce in Spark?

    Asked in 5 of 180 interviews · last asked Sep 18 · Concept · Mid-level

    Show answer

    For Spark DataFrames, repartition shuffles data and can increase or decrease the partition count. Coalesce reduces the count without a shuffle by combining existing partitions. I use repartition to spread work more evenly or partition by columns. I use coalesce when I need fewer partitions and want to avoid shuffle costs.

    • Repartition costs more because it moves data across the cluster.
    • Coalesce can leave uneven partitions; reducing too far can limit parallelism.
    • Unlike DataFrame coalesce, RDD coalesce supports an optional shuffle and can increase partitions when enabled.

    What the interviewer checks: The interviewer checks whether you can choose a partition strategy based on shuffle cost and parallelism.

    Other ways it was asked
    • “What is the difference between repartition and coalesce?”
    • “What is the difference between repartition and coalesce in Spark, and when would you use each?”
    • “When would you use repartition, and when would you use coalesce in Spark?”

  13. Which dbt tests would you use to validate models and source data?

    Asked in 5 of 180 interviews · last asked Sep 22 · Concept · Mid-level

    Show answer

    I use dbt’s built-in data tests: not_null, unique, accepted_values, and relationships. I apply them to models and sources to check required fields, duplicate keys, allowed values, and references to other tables. For business rules, I write custom SQL tests or reusable generic tests.

    • Use both unique and not_null to validate a primary key.
    • The relationships test checks matching values but skips nulls; add not_null when the reference is required.
    • Custom SQL data tests return failing rows; by default, any returned row fails the test.
    • Use source freshness checks separately to detect stale source data.

    What the interviewer checks: The interviewer checks whether you can choose tests that catch bad data and enforce business rules.

    Other ways it was asked
    • “What data quality tests are available in dbt?”
    • “How do you write data-quality tests in dbt?”
    • “What data quality tests can you implement in dbt?”

  14. How would you investigate a successful pipeline with incomplete results and prevent it from happening again?

    Asked in 5 of 180 interviews · last asked Sep 29 · Behavioral · Mid-level

    Show answer

    I would confirm the expected records and time range with the business user. Then I would compare counts and record IDs at each stage to find where data disappeared. I would fix the cause and backfill safely without duplicates. I would release the report after reconciliation and add completeness checks and alerts.

    • Check source delivery, filters, joins, rejected records, and incremental watermarks, including late arrivals.
    • Mark the report as incomplete or pause publication while investigating.
    • Validate recovered record IDs and business totals against the agreed scope, not just row counts.
    • Make completeness checks part of pipeline success, with clear thresholds and alert owners.

    What the interviewer checks: The interviewer checks whether you separate job success from data correctness, investigate systematically, and verify recovery before restoring trust.

    Other ways it was asked
    • “Tell me about a time a data pipeline appeared successful, but the business reported an incorrect result. How did you investigate and resolve the issue?”
    • “Tell me about a time a data pipeline appeared successful but produced incomplete results. What did you clarify with the business user, how did you find the cause, and how did you prevent it from happening again?”
    • “How would you find the root cause of a data pipeline that completed successfully but produced incomplete results, and what would you change?”

  15. Write a SQL query to find the second-highest salary.

    Asked in 4 of 180 interviews · last asked Sep 17 · Write code · Junior

    Show answer

    I find the highest salary below the maximum salary in the employee table. This returns the second-highest distinct salary, so employees with the same salary do not count as separate ranks.

    • Duplicate salaries count as one salary level.
    • MAX ignores NULL salaries.
    • The query returns NULL if fewer than two distinct non-NULL salaries exist.
    SQL
    SELECT MAX(salary) AS second_highest_salary
FROM employee
WHERE salary < (
    SELECT MAX(salary)
    FROM employee
);

    What the interviewer checks: The interviewer is checking whether you can find a ranked value while handling duplicates and missing results.

    Other ways it was asked
    • “Write a SQL query to find employees earning the second-highest salary in each department.”
    • “Write a query or code to find the second highest salary.”
    • “Write SQL code to get the second-highest salary from the employee table.”

  16. How would you optimize a slow-running SQL query?

    Asked in 4 of 180 interviews · last asked Sep 9 · Scenario · Mid-level

    Show answer

    I would check the execution plan and measure runtime to find where the query spends time. Then I would reduce unnecessary rows and columns, review joins, and check indexes and table statistics. I would test each change on realistic data and confirm that the results stay correct.

    • Look for expensive scans, poor row estimates, large sorts, and joins that produce too many rows.
    • Choose indexes based on filters and joins; indexes also add storage and slow writes.
    • Check for lock waits or resource limits, not just problems in the query.
    • Plan tools and optimizer behavior differ by database; compare runtime and resource use before and after changes.

    What the interviewer checks: The interviewer checks whether you use evidence to find bottlenecks and improve performance without changing results.

    Other ways it was asked
    • “How would you optimize a SQL query?”
    • “How do you optimize a long-running SQL query?”
    • “How would you optimize a slow SQL query?”

  17. What is SQL?

    Asked in 4 of 180 interviews · last asked Sep 19 · Concept · Junior

    Show answer

    SQL stands for Structured Query Language. It is used to read and change data in relational databases. It also lets you create tables, set rules for data, and manage access.

    • SQL can retrieve, insert, update, and delete data.
    • Relational databases organize data into tables with rows and columns.
    • SQL is declarative: you describe the result you want, not each step to get it.
    • SQL syntax and features vary between database systems.

    What the interviewer checks: The interviewer is checking whether you know what SQL stands for and how it is used.

    Other ways it was asked
    • “What does SQL stand for?”

  18. What is an index in SQL, and how does it work?

    Asked in 4 of 180 interviews · last asked Oct 7 · Concept · Junior

    Show answer

    An index is a data structure that helps a database find rows without scanning the whole table. A common type, a B-tree, keeps indexed values in order and links them to row data. Indexes can speed up reads, but they use storage and add work when data changes.

    • Indexes can help filters, joins, and sorting.
    • The database chooses whether to use an index; scanning may be cheaper when many rows match.
    • Column order matters in indexes built on multiple columns.
    • Inserts, deletes, and updates to indexed values require index maintenance.

    What the interviewer checks: The interviewer is checking whether you understand how indexes speed up reads and their storage and write costs.

    Other ways it was asked
    • “What is an index?”
    • “What is indexing?”
    • “Can you explain what an index is in SQL?”

  19. Could you define the ACID properties?

    Asked in 4 of 180 interviews · last asked Sep 14 · Concept · Junior

    Show answer

    ACID describes four transaction guarantees: atomicity, consistency, isolation, and durability. Atomicity means all changes succeed or none take effect; consistency means defined rules and constraints stay valid. Isolation controls how concurrent transactions interact, depending on the isolation level. Durability means committed changes survive crashes.

    • Atomicity applies to the whole transaction, not just one statement.
    • Consistency depends on database constraints and correct transaction logic.
    • Weaker isolation levels allow more concurrency but may expose intermediate or changing data.
    • Durability protects committed changes, not unfinished work.

    What the interviewer checks: The interviewer checks whether you understand how transactions keep data reliable during failures and concurrent work.

    Other ways it was asked
    • “What are ACID properties?”
    • “Explain ACID properties.”
    • “Can you define two of the ACID properties?”

  20. What is the difference between a data lake and a data warehouse?

    Asked in 4 of 180 interviews · last asked Oct 9 · Concept · Junior

    Show answer

    A data lake stores data in many formats, often before it is cleaned or organized. A data warehouse stores prepared, organized data for SQL analysis and reporting. Lakes offer more flexibility for exploration and machine learning; warehouses make consistent business reporting easier, though modern systems can support both uses.

    • Lakes can hold raw and processed data, including files, images, and tables.
    • Warehouses usually hold cleaned, structured data, but many also support semi-structured data.
    • Lakes often apply structure when reading; warehouses usually define structure before loading.
    • Both need data quality checks, access controls, and clear ownership.

    What the interviewer checks: The interviewer checks whether you can compare storage, data preparation, and typical uses without treating the distinction as absolute.

    Other ways it was asked
    • “What is the basic difference between a data lake and a data warehouse?”
    • “What is the difference between a data warehouse and a data lake?”

SQL interview questions for data engineers

SQL questions cover joins, aggregations, window functions, query writing, constraints, transactions, and query optimization. 179 questions.

  1. What is a subquery in SQL?

    Asked in 3 of 180 interviews · last asked Sep 18 · Concept · Junior

    Other ways it was asked
    • “What is a subquery, and how would you use one?”
    • “Why do we use subqueries in SQL?”

  2. What is the difference between a primary key and a unique constraint?

    Asked in 2 of 180 interviews · last asked Aug 26 · Concept · Junior

    Other ways it was asked
    • “What are the differences between a unique key and a primary key?”

  3. Write an SQL query to find the highest-paid employee in each department.

    Asked in 2 of 180 interviews · last asked Sep 17 · Write code · Junior

    Other ways it was asked
    • “How would you write a SQL query to find the names of employees who receive the second highest salary in each department?”
    • “Write a SQL query to find employees whose salary is greater than their department's average salary.”

  4. What is the operational difference between ROW_NUMBER, RANK, and DENSE_RANK?

    Asked in 2 of 180 interviews · last asked Sep 18 · Concept · Mid-level

    Other ways it was asked
    • “What is the difference between ROW_NUMBER(), DENSE_RANK(), and RANK() in SQL?”
    • “What is the difference between ROW_NUMBER(), RANK(), and DENSE_RANK()?”

  5. What is the difference between a function and a stored procedure?

    Asked in 2 of 180 interviews · last asked Sep 18 · Concept · Junior

    Other ways it was asked
    • “What's the difference between functions and stored procedures?”
    • “What is the difference between a stored procedure and a function?”

  6. How would you retrieve the latest record for each employee using GROUP BY in SQL?

    Asked in 2 of 180 interviews · last asked Oct 7 · Concept · Mid-level

    Other ways it was asked
    • “How would you use SQL to find the latest record for each user?”
    • “How can you use SQL to retrieve the latest record for each employee?”
    • “How can you retrieve the latest record for an employee ID using SQL?”

  7. How do you find duplicate records in a table?

    Asked in 2 of 180 interviews · last asked Oct 7 · Concept · Junior

    Other ways it was asked
    • “How would you find duplicate records in an employees table using SQL?”

  8. Can you tell me about partitioning and how you select the right column for partitioning a table?

    Asked in 2 of 180 interviews · last asked Sep 10 · Concept · Mid-level

    Other ways it was asked
    • “How would you choose columns for partitioning and clustering in a table?”

  9. What are aggregate functions? Can you give an example?

    Asked in 2 of 180 interviews · last asked Aug 26 · Concept · Junior

    Other ways it was asked
    • “What are aggregate functions?”

  10. What is the difference between a view and a materialized view?

    Asked in 2 of 180 interviews · last asked Sep 18 · Concept · Junior

All SQL interview questions

Python and coding interview questions for data engineers

Work through questions on Python, algorithms, data structures, APIs, frameworks, and software development basics. 147 questions.

  1. What are REST APIs, and when would you use them?

    Asked in 3 of 180 interviews · last asked Sep 21 · Concept · Junior

    Other ways it was asked
    • “What are REST APIs?”
    • “What are REST APIs, and how are they used to integrate data?”
    • “What are REST APIs and webhooks, and when would you use each?”

  2. How would you write Python code to reverse a string?

    Asked in 3 of 180 interviews · last asked Sep 10 · Write code · Junior

    Other ways it was asked
    • “How would you reverse a string in Python without using slicing or built-in functions?”
    • “How do I reverse a string in Python?”
    • “How would you reverse a string in Python without using slicing?”

  3. What are mutable and immutable objects in Python, and when would you use each?

    Asked in 3 of 180 interviews · last asked Sep 9 · Concept · Junior

    Other ways it was asked
    • “What is the difference between mutable and immutable objects in Python?”
    • “Give one example of a mutable data type and one example of an immutable data type.”
    • “Give an example of a mutable and an immutable data type, and explain how you can tell the difference.”

  4. What is the difference between a list and a tuple in Python?

    Asked in 2 of 180 interviews · last asked Sep 18 · Concept · Junior

    Other ways it was asked
    • “What is the difference between a list and a tuple?”
    • “What is the difference between lists and tuples in Python?”

  5. What is a lambda function in Python, and how do you use it?

    Asked in 2 of 180 interviews · last asked Oct 7 · Concept · Junior

    Other ways it was asked
    • “What are Python lambda functions, and when would you use them?”
    • “What is a lambda function?”

  6. What are *args and **kwargs in Python?

    Asked in 2 of 180 interviews · last asked Aug 26 · Concept · Junior

    Other ways it was asked
    • “What are `args` and `kwargs`?”
    • “What are *args and **kwargs used for when defining a Python function?”

  7. Can you implement the binary search algorithm in Python?

    Asked in 2 of 180 interviews · last asked Sep 14 · Write code · Junior

    Other ways it was asked
    • “Write Python code for binary search.”

  8. How do you remove duplicate items from a list in Python?

    Asked in 2 of 180 interviews · last asked Sep 30 · Write code · Junior

    Other ways it was asked
    • “What are other ways to remove duplicate items from a list in Python besides using a set?”
    • “How would you implement duplicate removal from a list using a loop and a new list in Python?”
    • “How would you find duplicate values in a Python list?”

  9. Write a function that solves the Two Sum problem. What is the time complexity of your solution?

    Asked once · last asked Sep 11 · Write code · Junior

    Other ways it was asked
    • “What is the time complexity of your solution to the two-sum problem?”

  10. What are the different types of bean scopes in Spring?

    Asked once · last asked Jul 16 · Concept · Mid-level

    Other ways it was asked
    • “What bean scopes does Spring support, and how do they affect bean lifecycles?”

  11. Can you explain the difference between HashMap and ConcurrentHashMap?

    Asked once · last asked Jul 16 · Concept · Mid-level

    Other ways it was asked
    • “What is the difference between Java's HashMap and ConcurrentHashMap?”

  12. What is the difference between checked and unchecked exceptions?

    Asked once · last asked Jul 16 · Concept · Junior

    Other ways it was asked
    • “What is the difference between checked and unchecked exceptions in Java?”
Show 7 more

  1. What is the difference between a monolithic architecture and microservices?

    Asked once · last asked Dec 15 · Design · Mid-level

    Other ways it was asked
    • “How does a microservices architecture differ from a monolithic application architecture?”

  2. Find the longest substring without repeated characters.

    Asked once · last asked Oct 7 · Write code · Junior

    Other ways it was asked
    • “Find the longest substring without repeating characters. Explain your approach.”

  3. What is the process through which a Spring Boot application starts?

    Asked once · last asked Jul 16 · Concept · Mid-level

    Other ways it was asked
    • “What happens internally when a Spring Boot application starts?”

  4. How would you use Python to process terabytes of data reliably?

    Asked once · last asked Aug 12 · Scenario · Senior

    Other ways it was asked
    • “How would you optimize Python code that processes terabytes of data?”

  5. What is the binary search algorithm, and when can it be used?

    Asked once · last asked Sep 2 · Concept · Junior

  6. Can Pandas query distributed data, and what tools or approaches would you use instead?

    Asked once · last asked Sep 9 · Concept · Mid-level

  7. How would you find the first non-repeating character in a string?

    Asked once · last asked Sep 11 · Write code · Mid-level

Data modeling and data warehouse interview questions

Data modeling questions explore fact-table grain, slowly changing dimensions, business metrics, warehouse design, and reporting. 126 questions.

  1. What is the difference between OLTP and OLAP systems?

    Asked in 3 of 180 interviews · last asked Sep 18 · Concept · Junior

    Other ways it was asked
    • “What is the difference between OLAP and OLTP?”
    • “What's the difference between OLAP and OLTP?”

  2. How would you design a dimensional model to solve a business problem, choose the fact table grain, and evaluate the trade-offs?

    Asked in 3 of 180 interviews · last asked Sep 29 · Design · Senior

    Other ways it was asked
    • “Tell me about a dimensional model you designed. How did you choose the fact table grain, and why did you separate facts from dimensions?”
    • “Tell me about a dimensional model you designed. What business problem did it solve, how did you choose the fact table grain, and what trade-offs did you consider?”
    • “Tell me about the dimensional model you designed, and how did you decide the fact table grain and what trade-offs did that decision create?”

  3. What is DAX, and when would you use it in Power BI?

    Asked in 2 of 180 interviews · last asked Sep 3 · Concept · Junior

    Other ways it was asked
    • “What are some key functions of DAX in Power BI?”
    • “What is DAX in Power BI, and what is it used for?”

  4. What's the difference between a fact table and a dimension table?

    Asked in 2 of 180 interviews · last asked Sep 18 · Concept · Junior

    Other ways it was asked
    • “What is the difference between a fact table and a dimension table?”
    • “What are dimension tables and fact tables, and how do they differ?”

  5. What is the difference between SCD Type 1 and SCD Type 2?

    Asked in 2 of 180 interviews · last asked Sep 20 · Concept · Mid-level

    Other ways it was asked
    • “What are SCD Type 1 and Type 2, and how do they differ?”

  6. How would you model corrections to an existing operational event and prevent duplicate fact records?

    Asked in 2 of 180 interviews · last asked Sep 21 · Scenario · Mid-level

    Other ways it was asked
    • “How would you handle corrections or repeated updates without creating duplicate fact records?”

  7. What are the benefits of Power BI?

    Asked in 2 of 180 interviews · last asked Sep 21 · Concept · Junior

    Other ways it was asked
    • “Can you please explain the features of Power BI?”

  8. In a star schema, when do you choose a surrogate key over a natural key for a dimension?

    Asked once · last asked Aug 23 · Concept · Mid-level

    Other ways it was asked
    • “In a star schema, when would you choose a surrogate key over a natural key for a dimension, and how would you maintain that choice during incremental loads?”

  9. What is your approach to providing an end-to-end data warehouse solution for a client?

    Asked once · last asked Sep 19 · Design · Mid-level

    Other ways it was asked
    • “What would be your approach to implementing a data warehouse from scratch and delivering an end-to-end solution?”

  10. How would you create a healthcare data warehouse?

    Asked once · last asked Sep 19 · Design · Mid-level

    Other ways it was asked
    • “How would you create a healthcare data warehouse for a client?”
    • “How do you create a data warehouse?”

  11. What is a data mart, and why can it make queries faster?

    Asked once · last asked Sep 19 · Concept · Junior

    Other ways it was asked
    • “What is a data mart, and why is it faster?”
    • “Why are data marts faster?”

  12. How would you define a unique operational event in a data model?

    Asked once · last asked Sep 21 · Concept · Mid-level

Show 5 more

  1. What source types and data models, such as fact, dimension, and event tables, are commonly used in data pipelines?

    Asked once · last asked Mar 3 · Concept · Mid-level

  2. How do you model data in a warehouse using fact and dimension tables, and how do you design schemas?

    Asked once · last asked Apr 14 · Concept · Mid-level

  3. What is the Data Vault modeling approach, and when would you use it?

    Asked once · last asked Apr 17 · Concept · Senior

  4. Analytics, Product, Compliance, and Finance define active player, credit earned, and credit redeemed differently, causing inconsistent reporting. How would you define a canonical dataset and a golden path for consumption?

    Asked once · last asked May 12 · Scenario · Senior

  5. What is the difference between identifying and non-identifying relationships in data modeling?

    Asked once · last asked May 16 · Concept · Junior

All Data Modeling interview questions

Snowflake and cloud data engineering interview questions

Compare Snowflake, BigQuery, Synapse, cloud infrastructure, storage formats, containers, and platform cost trade-offs. 182 questions.

  1. What is micro-partitioning in Snowflake?

    Asked in 4 of 180 interviews · last asked Sep 20 · Concept · Mid-level

    Other ways it was asked
    • “What is a micro-partition in Snowflake?”

  2. What is the difference between COPY INTO and Snowpipe in Snowflake?

    Asked in 3 of 180 interviews · last asked Sep 20 · Concept · Mid-level

    Other ways it was asked
    • “What's the difference between Snowpipe and the standard COPY INTO batch load?”
    • “What is the difference between Snowpipe and COPY INTO in Snowflake?”

  3. What is a virtual warehouse in Snowflake, and how does it work?

    Asked in 2 of 180 interviews · last asked Sep 20 · Concept · Mid-level

    Other ways it was asked
    • “What is a virtual warehouse in Snowflake?”
    • “What are the main benefits of a virtual warehouse in Snowflake?”

  4. What is Time Travel in Snowflake?

    Asked in 2 of 180 interviews · last asked Aug 31 · Concept · Mid-level

    Other ways it was asked
    • “What are the different methods for using Time Travel in Snowflake?”

  5. What is a staging area in Snowflake?

    Asked in 2 of 180 interviews · last asked Sep 18 · Concept · Junior

    Other ways it was asked
    • “What is staging in Snowflake?”

  6. What types of cache are available in Snowflake, and how do they work?

    Asked in 2 of 180 interviews · last asked Sep 22 · Concept · Mid-level

    Other ways it was asked
    • “What is caching in Snowflake, and how many types of caching are there?”

  7. What is a multi-cluster warehouse in Snowflake?

    Asked in 2 of 180 interviews · last asked Sep 18 · Concept · Mid-level

    Other ways it was asked
    • “What is multi-cluster in Snowflake?”

  8. Can you give me a few advantages of using Snowflake?

    Asked in 2 of 180 interviews · last asked Sep 14 · Concept · Junior

    Other ways it was asked
    • “Can you list five benefits of using Snowflake?”

  9. Can you tell me a little more about Docker?

    Asked in 2 of 180 interviews · last asked Sep 23 · Concept · Junior

    Other ways it was asked
    • “Can you explain how Docker works?”

  10. What is versioning in object storage?

    Asked in 2 of 180 interviews · last asked Sep 10 · Concept · Junior

    Other ways it was asked
    • “What is Object Versioning in Google Cloud Storage?”

  11. Why do you need Azure when you are already using Databricks to process and store data?

    Asked once · last asked Aug 26 · Concept · Mid-level

    Other ways it was asked
    • “Why do you need Azure storage if you can just store data in Databricks tables?”
    • “When you are already using Azure for the data lake and the middle layer architecture, why do you specifically need Databricks?”
    • “Why do you need Databricks if you are already using Azure Data Lake Storage for your middle layer?”

  12. Why might Amazon S3 be used as an intermediary between AWS Lambda and Snowflake, and how would you integrate it?

    Asked once · last asked Sep 20 · Design · Mid-level

    Other ways it was asked
    • “Why might you use Amazon S3 as an intermediary between AWS Lambda and Snowflake?”
    • “Why might you use Amazon S3 as an intermediary instead of loading data directly from AWS Lambda into Snowflake?”
    • “How would you set up data ingestion between AWS Lambda, Amazon S3, and Snowflake?”
Show 6 more

  1. For a workload that retrieves complete rows, would you recommend a row-based RDBMS?

    Asked once · last asked Jul 29 · Scenario · Mid-level

    Other ways it was asked
    • “For a workload that retrieves complete rows, would you recommend a row-based RDBMS such as MySQL or a columnar database?”

  2. How do you copy nested folder structures from one Cloud Storage bucket to another using the shell?

    Asked once · last asked Sep 10 · Tooling · Mid-level

    Other ways it was asked
    • “How would you copy nested folders and files from one Cloud Storage bucket to another?”

  3. What is the difference between a Deployment and a StatefulSet in Kubernetes?

    Asked once · last asked Sep 10 · Concept · Junior

    Other ways it was asked
    • “What is the difference between a Kubernetes Deployment and a StatefulSet?”

  4. What criteria would you use to choose Snowflake over other data warehouse solutions?

    Asked once · last asked Sep 20 · Scenario · Mid-level

  5. How do you decide whether to transform data formats when migrating data to Amazon S3?

    Asked once · last asked Mar 3 · Scenario · Mid-level

  6. How do you manage key rotation and application secrets using AWS KMS or another secrets-management service?

    Asked once · last asked Mar 3 · Tooling · Senior

ETL and data pipeline interview questions

Pipeline questions cover ingestion, transformations, orchestration, incremental loading, batch and streaming design, migrations, and end-to-end architecture. 243 questions.

  1. What is an idempotent data pipeline, and how does idempotency help?

    Asked in 3 of 180 interviews · last asked Oct 18 · Concept · Mid-level

    Other ways it was asked
    • “What does idempotency mean in a data integration pipeline?”
    • “What does it mean for a data pipeline to be idempotent? Give a concrete example.”

  2. What is Azure Data Factory?

    Asked in 3 of 180 interviews · last asked Aug 25 · Concept · Junior

    Other ways it was asked
    • “Please explain Azure Data Factory.”
    • “What is Azure Data Factory used for in a data engineering workflow?”

  3. What is the difference between batch processing and stream processing?

    Asked in 2 of 180 interviews · last asked Sep 17 · Concept · Mid-level

    Other ways it was asked
    • “What is the difference between batch and stream processing, and how do you read data with Spark?”

  4. How would you design a data ingest and transformation pipeline for a retail company using Azure Data Factory and Azure Databricks to process data from multiple sources and load the results into a Delta Lake, maximizing performance and handling failures effectively?

    Asked in 2 of 180 interviews · last asked Aug 11 · Design · Senior

    Other ways it was asked
    • “You're tasked with designing an ETL pipeline using Azure Databricks to process incremental data loads from Azure Data Lake. The data contains multiple large Parquet files, and you need to perform transformations such as filtering, aggregations, and data enrichment before writing the output back to Azure SQL database. How would you ensure efficient data processing, optimize costs, and handle potential failures in the pipeline?”
    • “How would you structure an Azure Data Factory pipeline to optimize performance and ensure fault tolerance when ingesting large volumes of data from multiple sources, transforming it, and loading it into Azure SQL Database?”
    • “How would you design an Azure Data Factory pipeline for data ingestion from on-premise SQL Server, processing in Azure Databricks, and loading into Azure Synapse Analytics to ensure security, scalability, and performance?”

  5. How do you build an ETL pipeline?

    Asked in 2 of 180 interviews · last asked Sep 18 · Design · Mid-level

    Other ways it was asked
    • “How do you process data in an ETL pipeline, from extraction through transformation and loading?”
    • “What platforms or tools do you use when you build an ETL pipeline?”

  6. If business data is maintained manually in Excel or CSV files on SharePoint, how would you build a reliable reporting pipeline that handles changes to file structure or values?

    Asked in 2 of 180 interviews · last asked Sep 29 · Scenario · Mid-level

    Other ways it was asked
    • “Some operational data arrives in Excel or CSV files maintained by business users, perhaps through SharePoint. How would you bring it into a production reporting pipeline and make sure people can trust it?”
    • “Tell me about a time you worked with data maintained in multiple CSV files on SharePoint because it wasn’t captured in a core system. What problem did it cause, what did you change, and how did you make the resulting reporting trustworthy?”
    • “If business data is maintained manually in Excel or CSV files on SharePoint, how would you bring it into a reliable reporting pipeline? Tell me about your experience with this kind of data and what you did when the file structure or values changed.”

  7. What is an integration runtime in Azure Data Factory?

    Asked in 2 of 180 interviews · last asked Oct 7 · Concept · Junior

    Other ways it was asked
    • “How do you set up an integration runtime in Azure Data Factory?”
    • “What is a self-hosted integration runtime in Azure Data Factory, and when would you use it?”
    • “What are the different types of Azure Data Factory integration runtime?”

  8. What are some commonly used activities in Azure Data Factory?

    Asked in 2 of 180 interviews · last asked Oct 7 · Tooling · Junior

    Other ways it was asked
    • “Which Azure Data Factory activities would you use to build a data pipeline?”
    • “What are five commonly used activities in Azure Data Factory?”

  9. What is data engineering?

    Asked in 2 of 180 interviews · last asked Sep 14 · Concept · Junior

    Other ways it was asked
    • “What is data science engineering?”

  10. How do you configure connectivity from Azure Data Factory to an on-premises SQL Server, and what connection parameters are required?

    Asked once · last asked Aug 19 · Tooling · Mid-level

    Other ways it was asked
    • “How do you establish connectivity and move data between Azure Data Factory and an on-premises SQL Server?”
    • “How do you configure connectivity between Azure Data Factory and an on-premises SQL Server?”

  11. If shipment records are being added or updated while your pipeline is moving through the API pages, how would you reduce the risk of missing records or creating duplicates?

    Asked once · last asked Sep 22 · Scenario · Senior

    Other ways it was asked
    • “If shipment records are added or updated while a pipeline is paging through an API, how would you reduce the risk of missing records or creating duplicates?”
    • “If shipment records are being added or updated while your pipeline is moving through the API pages, how would you reduce the risk of missing or duplicate records?”

  12. What factors can motivate moving Apache NiFi from on-premises infrastructure to the cloud, and what challenges might arise?

    Asked once · last asked Feb 6 · Scenario · Mid-level

Show 7 more

  1. What are the key challenges you typically encounter when building data integration pipelines?

    Asked once · last asked May 19 · Scenario · Senior

    Other ways it was asked
    • “What other challenges can arise when building data integration pipelines?”

  2. Why would you use DynamoDB Streams, and what benefits do they provide?

    Asked once · last asked Jun 23 · Concept · Mid-level

  3. What would you want to understand before migrating R pipelines to dbt or PostgreSQL?

    Asked once · last asked Sep 9 · Scenario · Mid-level

    Other ways it was asked
    • “What would you assess, and what issues would you look for, when migrating R pipelines to dbt or PostgreSQL?”
    • “How would you approach migrating an R data pipeline to PostgreSQL?”

  4. How would you ingest changes from an on-premises SQL Server database, and why choose direct reads or transaction-log CDC?

    Asked once · last asked Sep 16 · Scenario · Senior

    Other ways it was asked
    • “How would you design ingestion from an on-premises SQL Server database, choosing between direct reads and transaction-log CDC?”

  5. What kinds of batch and real-time integrations can Apache NiFi support, and how would you implement them?

    Asked once · last asked Feb 6 · Tooling · Mid-level

  6. How would you design an end-to-end Apache NiFi flow to process a 4-GB file?

    Asked once · last asked Feb 6 · Tooling · Mid-level

  7. Which Apache NiFi processors would you use to read a 4-GB file from SFTP in batches and send the processed data to a target system?

    Asked once · last asked Feb 6 · Tooling · Mid-level

All ETL and Data Pipelines interview questions

Spark, PySpark, and Databricks interview questions

Explore distributed processing, PySpark coding, Spark tuning, Databricks, Delta Lake, Iceberg, and lakehouse table management. 195 questions.

  1. How would you overcome data skew in Spark?

    Asked in 4 of 180 interviews · last asked Sep 10 · Scenario · Senior

    Other ways it was asked
    • “Besides salting, what are other ways to handle data skewness in Spark?”
    • “How would you handle data skew in Spark so that processing tasks finish at roughly the same time?”
    • “In the Spark UI, what would you look at to identify data skew?”

  2. How does a broadcast join work in Spark, and when would you use one?

    Asked in 4 of 180 interviews · last asked Aug 14 · Concept · Mid-level

    Other ways it was asked
    • “What does a broadcast join do in Spark?”
    • “What is a broadcast join in Apache Spark, and when would you use one?”
    • “What exactly happens when you use a broadcast join?”

  3. What is Apache Iceberg, and what are its key features for managing data lake tables?

    Asked in 4 of 180 interviews · last asked Jul 16 · Concept · Senior

    Other ways it was asked
    • “What have you built with Apache Iceberg, and how would you get up to speed using it as the table format across a data lake?”
    • “What problems does Apache Iceberg solve that Delta Lake does not?”
    • “How is Apache Iceberg different from Hive external tables?”

  4. What is Unity Catalog, and is it exclusive to Databricks?

    Asked in 3 of 180 interviews · last asked Sep 18 · Concept · Mid-level

    Other ways it was asked
    • “What is the role of Unity Catalog in Databricks?”
    • “What is Unity Catalog?”

  5. What is lazy evaluation in Apache Spark?

    Asked in 2 of 180 interviews · last asked Sep 17 · Concept · Mid-level

    Other ways it was asked
    • “What is lazy evaluation in Spark, and how does it work?”

  6. Write a PySpark script to read a large CSV file from Azure Data Lake Storage.

    Asked in 2 of 180 interviews · last asked Aug 22 · Write code · Mid-level

    Other ways it was asked
    • “Write a PySpark script to read a large batch of JSON files from an Azure Data Lake Gen2 container, validate the JSON schema, write to a Delta Lake table partitioned by a column, and include error handling for schema mismatches with a summary of invalid records.”
    • “Write a PySpark script to read a large batch of JSON files from an Azure Data Lake Gen2 container, validate the JSON schema, write to a Delta Lake table partitioned by a column, and handle schema mismatches by summarizing invalid records.”
    • “Write a PySpark script to read a parquet file from Azure Data Lake, filter rows where the sales amount is greater than 5,000, and save the result as a new CSV file back to Azure Data Lake. Ensure your solution is efficient and handles large datasets.”

  7. What are Delta Lake deletion vectors, and when would you use them?

    Asked in 2 of 180 interviews · last asked Aug 20 · Concept · Senior

    Other ways it was asked
    • “What are Delta deletion vectors, and how do they work?”

  8. How does Apache Iceberg store data internally?

    Asked in 2 of 180 interviews · last asked Aug 20 · Concept · Senior

    Other ways it was asked
    • “How does Apache Iceberg work, specifically regarding data storage and search functionality?”

  9. What is the difference between partitioning and liquid clustering?

    Asked in 2 of 180 interviews · last asked Aug 20 · Concept · Senior

    Other ways it was asked
    • “How does liquid clustering differ from traditional partitioning?”
    • “If both partitioning and liquid clustering are available, when would partitioning be the better choice?”

  10. How would you optimize Spark jobs in Databricks when dealing with large datasets?

    Asked in 2 of 180 interviews · last asked Aug 12 · Scenario · Senior

    Other ways it was asked
    • “What Databricks or PySpark optimizations have you carried out?”
    • “How have you optimized Spark jobs in Databricks when dealing with large datasets?”

  11. What is the medallion architecture for data processing?

    Asked in 2 of 180 interviews · last asked Sep 17 · Concept · Mid-level

    Other ways it was asked
    • “What is a medallion architecture, and how do its layers organize data?”

  12. How would you design a real-time stream processing solution for IoT device data using PySpark and Azure Databricks, including aggregations, fault tolerance, and schema evolution handling?

    Asked in 2 of 180 interviews · last asked Aug 11 · Design · Senior

    Other ways it was asked
    • “You are tasked with designing a real time data processing pipeline using Azure Databricks and PySpark. The data comes from an IoT device stream with thousands of records per second, containing information such as device_id, temperature, and timestamp. The requirements are to identify devices with abnormal temperature readings, e.g. above a threshold, and store these anomalies in a separate Azure SQL database table for further analysis. How would you design this pipeline to ensure low latency, scalability, and fault tolerance? Please include any PySpark transformations or optimizations you would use.”
Show 7 more

  1. What is the difference between external tables and managed tables?

    Asked in 2 of 180 interviews · last asked Aug 20 · Concept · Junior

    Other ways it was asked
    • “What is the difference between managed and external tables in Databricks?”
    • “What are the benefits of managed tables versus external tables?”

  2. What do you mean by data skew?

    Asked in 2 of 180 interviews · last asked Sep 11 · Concept · Mid-level

    Other ways it was asked
    • “Can you give an example of data skew?”
    • “What is data skewness?”

  3. What is Apache Spark?

    Asked in 2 of 180 interviews · last asked Sep 17 · Concept · Junior

  4. Can you explain what Databricks is?

    Asked in 2 of 180 interviews · last asked Aug 15 · Concept · Junior

    Other ways it was asked
    • “What is it that you know about the Databricks platform?”
    • “How does Databricks differ from other cloud data platforms?”

  5. What is a Delta table?

    Asked in 2 of 180 interviews · last asked Oct 1 · Concept · Junior

    Other ways it was asked
    • “Can you tell me what advantages Delta Tables bring?”

  6. How do we create a table in Databricks?

    Asked in 2 of 180 interviews · last asked Aug 26 · Tooling · Junior

    Other ways it was asked
    • “Which command do we use to create a managed table in Databricks?”
    • “How is data physically stored when you create a table in Databricks?”

  7. Can you explain Spark architecture?

    Asked in 2 of 180 interviews · last asked Sep 10 · Concept · Mid-level

    Other ways it was asked
    • “How does Spark work under the hood, in a nutshell?”

All Spark, PySpark and Databricks interview questions

Data quality, testing, and pipeline monitoring interview questions

Reliability questions focus on dbt, validation, reconciliation, monitoring, incident diagnosis, safe deployments, recovery, access controls, and data governance. 294 questions.

  1. How would you validate a data migration to ensure data quality and no data loss?

    Asked in 3 of 180 interviews · last asked Aug 20 · Scenario · Mid-level

    Other ways it was asked
    • “How do you validate data quality during a data migration?”
    • “How do you ensure data quality during a data migration?”
    • “How would you verify that a server-to-cloud data migration completed without data loss?”

  2. How would you recover a partially loaded API batch without missing or duplicate records, and advance checkpoints safely?

    Asked in 3 of 180 interviews · last asked Sep 29 · Scenario · Senior

    Other ways it was asked
    • “Suppose the API fails after five pages have been retrieved. How would you recover the batch without losing records or creating duplicates?”
    • “Tell us about a time an API or data pipeline failed after some data had already arrived. How did you decide whether to rerun it, and how did you check that the recovered report was complete and consistent?”
    • “If a new engineer has to support an incremental ADF-to-Snowflake pipeline while you are away, what would you document so they can detect a partial batch, rerun it safely, and prove the recovered data is complete?”

  3. How would you handle an urgent reporting request for raw data without bypassing access controls?

    Asked in 3 of 180 interviews · last asked Sep 22 · Scenario · Senior

    Other ways it was asked
    • “A business analyst needs urgent access to raw data for an important report, but your governance model does not allow direct raw schema access. How would you handle the request without unnecessarily delaying their work?”
    • “A business analyst needs urgent access to a raw data source for an important report, but your governance model does not allow direct raw schema access. How would you handle the request without unnecessarily delaying their work?”
    • “A reporting analyst asks for urgent direct access to the raw Snowflake schema because the curated dataset does not contain a few fields needed for tomorrow's report. How would you respond?”

  4. How do you monitor data pipelines and configure alerting?

    Asked in 3 of 180 interviews · last asked Sep 22 · Scenario · Senior

    Other ways it was asked
    • “How would you implement monitoring and alerting for a data engineering environment?”
    • “How would you monitor a data pipeline and set up alerts for load failures or delays?”

  5. What is dbt, and why would you use it?

    Asked in 3 of 180 interviews · last asked Sep 20 · Concept · Junior

    Other ways it was asked
    • “What is dbt?”
    • “Can you explain what dbt is?”

  6. How would you prove that a data quality control is operating effectively in production rather than just showing that the control has been configured?

    Asked in 2 of 180 interviews · last asked Sep 22 · Scenario · Senior

    Other ways it was asked
    • “How do you prove that a data quality control is actually working in production, rather than just showing that the rule or test exists?”
    • “How would you demonstrate to an auditor or service owner that a data quality control is operating effectively in production, rather than merely showing that a dbt test exists?”
    • “How would you prove to a service owner or auditor that a data quality control was actually operating effectively in production rather than just showing that the rule existed?”

  7. How would you safely deploy a change to a dbt model that feeds several business reports, and what would you do if the change created duplicate records after release?

    Asked in 2 of 180 interviews · last asked Sep 22 · Scenario · Senior

    Other ways it was asked
    • “How would you deploy a dbt model change safely when the model feeds several downstream reports?”
    • “A dbt model feeds several important reports. How would you release a change safely, and what would you do if the change introduced duplicate records after deployment?”
    • “When deploying a dbt model change that feeds several downstream reports, what would you test before release, and how would you recover if it produced incorrect results?”

  8. How would you handle missing fields in data scraped from a website?

    Asked in 2 of 180 interviews · last asked Aug 29 · Scenario · Mid-level

    Other ways it was asked
    • “How do you handle missing or inconsistently formatted fields in scraped data?”

  9. What is the ref function used for in dbt?

    Asked in 2 of 180 interviews · last asked Sep 20 · Concept · Mid-level

    Other ways it was asked
    • “What is the difference between source and ref in dbt?”

  10. If a new customer dimension causes the Power BI shipment total to double even though the raw shipment count is unchanged, how would you escalate the issue, protect the report, and prove your diagnosis is correct?

    Asked in 2 of 180 interviews · last asked Sep 29 · Scenario · Senior

    Other ways it was asked
    • “A Power BI report shows shipment totals 20% higher than the detailed order records, even though the report refresh succeeded. How would you escalate the problem, find the root cause, and prove the corrected number is safe to publish?”

  11. What is the purpose of data cleaning?

    Asked in 2 of 180 interviews · last asked Sep 4 · Concept · Junior

    Other ways it was asked
    • “Why do we need data cleaning?”
    • “Why is data cleaning important?”

  12. How would you design observability and monitoring for a data platform, including when to implement it, how to assess current coverage, and how to identify gaps?

    Asked once · last asked Jul 28 · Scenario · Senior

Show 6 more

  1. How would you identify trends in recurring data issues and improve the process?

    Asked once · last asked Sep 9 · Scenario · Mid-level

    Other ways it was asked
    • “How would you identify trends in recurring data issues and determine what development is needed to address them?”

  2. How do you securely manage credentials when connecting data pipelines to Salesforce and databases?

    Asked once · last asked Sep 10 · Scenario · Mid-level

    Other ways it was asked
    • “How would you implement secure credential management for data pipelines connecting to Salesforce and databases?”

  3. When would you use an incremental model in dbt?

    Asked once · last asked Sep 20 · Concept · Mid-level

    Other ways it was asked
    • “Can you give an example of when an incremental model in dbt is appropriate?”

  4. How would you create a custom SQL test in dbt?

    Asked once · last asked Sep 20 · Tooling · Mid-level

  5. A source record is corrected today, but it belongs to last month. The dashboard still shows the old value after its scheduled refresh. How would you investigate and fix it?

    Asked once · last asked Sep 23 · Scenario · Senior

  6. If SQL checks find no transactions linked to today’s duplicate IDs, but today’s feed includes 200 new customers who were not in yesterday’s dimension, would you use yesterday’s dimension, and what would you tell the service owner?

    Asked once · last asked Sep 29 · Scenario · Senior

    Other ways it was asked
    • “If today's fact contains transactions for 200 customers absent from yesterday's dimension and the duplicate keys have no matching transactions, would you use yesterday's dimension for the 5 p.m. feed, or what would you tell the service owner?”

AI, machine learning, and analytics interview questions

Review questions on AI applications, RAG, semantic search, model evaluation, data preparation, and interpreting evidence. 63 questions.

  1. How do you use AI in your work?

    Asked in 2 of 180 interviews · last asked Aug 26 · Tooling

    Other ways it was asked
    • “How do you use AI in your day-to-day work, and what is a specific use case?”
    • “How do you use AI in your day-to-day work, and what use cases have been helpful?”
    • “How do you use AI in your data engineering work?”

  2. Which metrics would you use to evaluate an AI agent?

    Asked once · last asked Oct 2 · Concept · Senior

    Other ways it was asked
    • “Which metrics would you use to evaluate an AI model?”

  3. How would you implement a RAG system step by step?

    Asked once · last asked Oct 18 · Scenario · Mid-level

    Other ways it was asked
    • “How would you implement a RAG system end to end?”

  4. Which metrics would you monitor during AI model training?

    Asked once · last asked Oct 2 · Concept · Mid-level

  5. What roles do support and confidence play in association rule mining?

    Asked once · last asked May 2 · Concept · Mid-level

    Other ways it was asked
    • “How are support and confidence used in association rule mining?”
    • “What is all-confidence in association rule mining?”

  6. What types of constraints are used in association rule mining?

    Asked once · last asked May 2 · Concept · Mid-level

  7. What are large language models (LLMs) and retrieval-augmented generation (RAG), and how do they differ?

    Asked once · last asked Sep 22 · Concept · Mid-level

  8. What AI functions are available in Snowflake Cortex?

    Asked once · last asked Sep 1 · Concept · Mid-level

  9. How would you design and implement an internal low-code/no-code generative AI application to accelerate functional scoping for projects?

    Asked once · last asked Sep 20 · Design · Senior

  10. What types of operations are performed in online analytical mining (OLAM)?

    Asked once · last asked May 2 · Concept · Mid-level

  11. What techniques can be used to measure correlation?

    Asked once · last asked May 2 · Concept · Mid-level

  12. How can you determine whether a measured correlation is statistically significant?

    Asked once · last asked May 2 · Concept · Mid-level

Show 8 more

  1. Which measures, such as lift and chi-square, can be used to assess associations in rule mining?

    Asked once · last asked May 2 · Concept · Mid-level

  2. What is the chi-square measure, and how do you evaluate it?

    Asked once · last asked May 2 · Concept · Mid-level

  3. How would you use AI tools or agents to accelerate development, testing, analysis, incident response, or documentation while protecting correctness and security?

    Asked once · last asked May 12 · Scenario · Senior

  4. How would you design an agentic AI workflow with LangChain, CrewAI, or a similar framework, covering its use case, orchestration, tool usage, memory, and reliability?

    Asked once · last asked May 27 · Design · Senior

    Other ways it was asked
    • “Describe an agentic AI workflow you built with LangChain, CrewAI, or a similar framework, including its use case, orchestration, tool usage, memory, and reliability.”

  5. How would you provide governed access to data and tools for generative AI and agentic workloads using Amazon Bedrock AgentCore, and what role could MCP servers play?

    Asked once · last asked Jun 16 · Design · Senior

  6. How do you use GitHub Copilot: with feature-driven development, or by asking it questions and having it write code?

    Asked once · last asked Jun 23 · Tooling · Mid-level

  7. How would you optimize pgvector-based retrieval with HNSW indexing for search performance and efficiency?

    Asked once · last asked Jul 30 · Scenario · Senior

  8. What key benefits and trade-offs would lead you to choose HNSW indexing over other indexing options?

    Asked once · last asked Jul 30 · Concept · Mid-level

    Other ways it was asked
    • “When selecting HNSW indexing over other options, what key benefits or considerations made it the right choice for your project?”

Data engineer behavioral and scenario interview questions

Project and scenario questions ask about technical ownership, stakeholder disagreements, incident communication, priorities, delivery planning, and learning unfamiliar platforms. 167 questions.

  1. Tell me about a time when different stakeholders disagreed about the definition of the same KPI. What was the disagreement, how did you help them reach a decision, and how did you implement the agreed definition in the data?

    Asked in 4 of 180 interviews · last asked Sep 29 · Behavioral · Mid-level

    Other ways it was asked
    • “Tell me about a time stakeholders had different definitions of the same KPI. How did you align them and create a reliable dataset?”
    • “Tell me about a time stakeholders disagreed about the definition of a KPI.”
    • “How did you resolve a stakeholder disagreement about a KPI definition and implement the agreed rule in the data?”

  2. Describe a production data incident you investigated, how you restored service, and how you prevented recurrence.

    Asked in 3 of 180 interviews · last asked Sep 22 · Behavioral · Senior

    Other ways it was asked
    • “Tell me about a production data incident you investigated, how you contained its impact and restored service, and how you prevented recurrence.”
    • “Tell me about a production data incident you personally investigated. When the issue was first reported, how did you assess the business impact, identify the root cause, recover the data safely, and prevent the issue from recurring?”
    • “Can you give a concrete example of a production issue you encountered, such as data latency, a schema mismatch, or a pipeline failure?”

  3. How would you respond if an operations manager insisted on using the original scheduled ETA for forward planning despite evidence supporting a different ETA?

    Asked in 2 of 180 interviews · last asked Sep 22 · Scenario · Mid-level

    Other ways it was asked
    • “What would you do if an operations manager insisted on using the original promised date for forward planning, even after you showed that revised ETAs were more accurate?”

  4. How would you transfer orchestration experience from Azure Data Factory to Airflow, identify learning gaps, and become productive safely?

    Asked in 2 of 180 interviews · last asked Sep 22 · Scenario · Mid-level

    Other ways it was asked
    • “Which data orchestration skills transfer from Azure Data Factory to Airflow and Spark, where would you expect learning gaps, and how would you become productive without creating delivery risk?”

  5. Tell me about a time you underestimated a data-delivery commitment: what assumption was wrong, when did you notify stakeholders, what did you deliver first, and how did you change your estimation approach?

    Asked in 2 of 180 interviews · last asked Sep 29 · Behavioral · Mid-level

    Other ways it was asked
    • “Tell me about a time you underestimated a piece of work or missed an expected delivery date. What did you get wrong, when did you tell the people affected, and what changed in how you work afterward?”
    • “Tell me about a time you underestimated a data project or made a delivery commitment. What assumption did you get wrong, when did you tell stakeholders, what did you deliver first, and what changed in how you estimate work now?”

  6. Tell me about a time you designed an end-to-end ETL pipeline, from extraction through data delivery, integrating multiple data sources while ensuring data quality.

    Asked in 2 of 180 interviews · last asked Sep 20 · Behavioral · Senior

    Other ways it was asked
    • “Can you give a detailed end-to-end example of a data pipeline you built, including how you ingested and transformed data, delivered it to downstream users, and ensured data quality?”

  7. Tell me about a technical decision you personally owned. What alternatives did you consider, why did you choose your approach, and how did you validate it?

    Asked in 2 of 180 interviews · last asked Oct 1 · Behavioral · Senior

    Other ways it was asked
    • “Tell me about a difficult technical decision you made on a software project and why you chose that approach.”

  8. If operations needs to publish a shipment-planning report today but finance’s control total still differs from the report by 4% and the cause is unknown, how would you explain the options and reach a release decision without ignoring the discrepancy?

    Asked in 2 of 180 interviews · last asked Sep 29 · Scenario · Senior

    Other ways it was asked
    • “Suppose the corrected report is accurate, but operations needs it today and finance says its control total still differs from the valid order lines. How would you manage the disagreement and decide whether anything can be released?”

  9. Tell me about a time a business stakeholder reported a data problem even though the technical process appeared successful. How did you establish what was actually wrong before you started troubleshooting?

    Asked once · last asked Sep 22 · Behavioral · Mid-level

    Other ways it was asked
    • “Tell me about a time a business stakeholder reported a data problem even though the technical process appeared successful. How did you investigate?”

  10. How do you work with user stories, understand business needs, and prioritize the work?

    Asked once · last asked Feb 19 · Behavioral · Mid-level

  11. Can you provide an example of a challenging feature you developed using Python and explain how you approached its design and implementation?

    Asked once · last asked Feb 27 · Behavioral · Mid-level

  12. How should responsibility for monitoring and maintaining an application be divided between an SRE team and the application engineering team?

    Asked once · last asked Mar 3 · Scenario · Senior

Show 8 more

  1. Tell me about a solution you designed, developed, and released. How did you gather feedback and organize the work through delivery?

    Asked once · last asked Mar 3 · Behavioral · Mid-level

  2. Tell me about a job or solution that used more resources than expected. How did you investigate and optimize its resource consumption?

    Asked once · last asked Mar 3 · Behavioral · Senior

  3. Tell me about a data solution you worked on as part of a team, including your role, how you collaborated, and the design decisions or trade-offs you made.

    Asked once · last asked Mar 20 · Behavioral · Mid-level

  4. Monday morning, Compliance flags mismatches in Friday’s daily sweepstakes credit report against Finance’s reconciliation. The product dashboard and payment system disagree, and ClickHouse contains duplicate-looking user records. As the staff engineer escalation point, how would you handle the incident?

    Asked once · last asked May 12 · Scenario · Senior

    Other ways it was asked
    • “On Monday morning, Compliance reports that Friday's daily sweepstakes credit report does not match Finance's reconciliation numbers. The product dashboard shows one number, the payment system shows another, and ClickHouse has duplicate-looking records for some users. As the staff engineer escalation point, how would you handle the incident?”

  5. During a data reconciliation incident involving conflicting reports and duplicate-looking ClickHouse records, what would you prioritize in the first 30 minutes?

    Asked once · last asked May 12 · Scenario · Senior

  6. Given a situation where the business provides a UI and says what data it needs but does not provide detailed user stories, how would you approach the work?

    Asked once · last asked May 19 · Scenario · Senior

  7. How can you manage the data engineers if you don’t understand what they’re doing?

    Asked once · last asked May 21 · Scenario · Senior

  8. Can you share a specific example of a time you addressed a team member’s performance issue, coached them, or identified a plan for improvement, and describe the outcome?

    Asked once · last asked May 22 · Behavioral · Senior

Live coding round next? Coding mode in the MeetAssist desktop app solves the problem on your screen, with your phone as the remote.

How this list is made

Every question here was asked by a real interviewer in a real job interview that a candidate ran MeetAssist in. After the call, the interviewer's questions are pulled from the transcript. Practice sessions, tests, exams and anything that was not a job interview are dropped.

Questions are rewritten so they make sense on their own: names of people, employers and clients are removed, and speech-to-text mistakes are fixed. Small talk, logistics and questions about one candidate's own résumé are left out. Rephrasings of the same question are merged, and the count is the number of different interviews it came up in.

The interviews come from candidates who use MeetAssist, so they lean towards software, data and AI roles: the shares describe these interviews, not every Data Engineer interview. Answers to the most asked questions are written and checked by an editor; a "common mistake" is shown only when reviews of real answers in these interviews show it, with the count.

180 interviews, 74 candidates, Nov 2025 to Oct 2026. Updated every month with new interviews.