All 54 questions
How do you work with a large dataset, and why would you choose that approach?
Asked in 2 of 307 interviews · last asked Aug 9 · Scenario · Mid-level
Other ways it was asked
- “How would you work with a large dataset?”
What is query folding, and when is it useful?
Asked in 2 of 307 interviews · last asked Sep 15 · Concept · Mid-level
Other ways it was asked
- “What is query folding?”
What tools and strategies would you use to develop or optimize ETL pipelines to improve data processing and analytics efficiency?
Asked in 2 of 307 interviews · last asked Feb 27 · Behavioral · Senior
Other ways it was asked
- “Describe a time when you developed or optimized ETL pipelines to improve data processing and analytics efficiency. What specific tools and strategies did you use?”
- “Can you describe a specific project where you optimized ETL performance? What steps did you take to ensure the processes met the application's performance requirements?”
What do you mean by data skew?
Asked in 2 of 307 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?”
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?”
Can Pandas query distributed data, and what tools or approaches would you use instead?
Asked once · last asked Sep 9 · Concept · Mid-level
What performance considerations would you account for when applying CDC changes?
Asked once · last asked Mar 20 · Scenario · Senior
What problems would you anticipate when processing a 4- to 5-GB nested JSON file?
Asked once · last asked Apr 17 · Scenario · Mid-level
When you run ETL on Spark or a cloud warehouse, what steps do you take to diagnose and fix performance regressions involving skew, shuffles, partitioning, or file sizes?
Asked once · last asked May 3 · Scenario · Senior
How would you process domain tables containing roughly 1.3–1.5 billion records when the reference tables are much smaller?
Asked once · last asked May 18 · Design · Senior
How would you handle unpredictable incoming data volumes, such as 10,000 transactions one hour, a million the next, and none the following hour?
Asked once · last asked May 18 · Design · Senior
What steps would you take if a mapping or workflow that should finish in 30 minutes suddenly took two hours?
Asked once · last asked Nov 10 · Scenario · Senior
If an SSIS package's performance varies significantly, how can you find the cause?
Asked once · last asked May 27 · Scenario · Mid-level
How would you achieve and measure a 60% reduction in compute time for a data-processing workload?
Asked once · last asked Jun 1 · Scenario · Senior
How would you reduce shuffle operations in a distributed data-processing job, and when might you change the partitioning strategy or processing logic?
Asked once · last asked Jun 1 · Scenario · Senior
Other ways it was asked
- “What are data shuffling and partitioning in a distributed data-processing system, how can they cause performance problems, and what changes can optimize them?”
- “How would you diagnose data skew and excessive shuffling in a distributed data-processing job, and what fixes would you consider?”
- “In distributed data processing, how can repartitioning reduce the number of shuffles, and doesn't repartitioning itself require a full data shuffle?”
In distributed data processing, does repartitioning change data stored on disk or data held in memory?
Asked once · last asked Jun 1 · Concept · Senior
How does repartitioning redistribute data, how can you distinguish data skew from simply having a large volume of data, and why might a shuffle stage spill to disk?
Asked once · last asked Jun 1 · Concept · Senior
Given a system serving over 1,000 customers and more than 1 billion vectors, how would you scale and optimize the document-processing and embedding pipeline?
Asked once · last asked Jun 12 · Design · Senior
How would you identify and handle failure points when scaling a document-processing pipeline from 50 documents to hundreds of thousands or millions?
Asked once · last asked Jun 12 · Scenario · Senior
How would you monitor and optimize data integration performance?
Asked once · last asked Jun 21 · Scenario · Mid-level
How would you reduce the processing time of a data workflow that currently takes three days?
Asked once · last asked Jun 22 · Scenario · Senior
Other ways it was asked
- “How would you reduce a data-processing workflow that currently takes three days?”
In a large ETL merge or upsert into a fact table, how would you decide between MERGE and INSERT APPEND plus a separate UPDATE, and what performance risks would you watch for?
Asked once · last asked Jun 25 · Scenario · Senior
How would you handle a very large PDF, like a 10 GB file, in your extraction pipeline?
Asked once · last asked Jul 17 · Design · Senior
If you had to process a PDF with many images and every image required an API call, how would you reduce the latency problem?
Asked once · last asked Jul 17 · Design · Senior
How would you process multiple pages of a PDF in parallel using workers, and when would you use this approach?
Asked once · last asked Jul 17 · Design · Mid-level
How would you scale data pipelines and enterprise data warehouse solutions?
Asked once · last asked Jul 29 · Design · Senior
Imagine you are tasked with optimizing an Azure Data Factory pipeline that processes a daily large volume of data. How would you approach this?
Asked once · last asked Aug 10 · Scenario · Senior
What optimization techniques can you apply to data transformations?
Asked once · last asked Aug 14 · Concept · Mid-level
What S3 storage classes or considerations matter for a data engineering pipeline?
Asked once · last asked Aug 20 · Concept · Mid-level
How do you implement chunked processing to aggregate results without holding the full dataset in memory?
Asked once · last asked Aug 23 · Scenario · Mid-level
What limitations can arise when using Replication Flows and Transformation Flows in SAP Datasphere with large datasets?
Asked once · last asked Aug 28 · Scenario · Senior
Other ways it was asked
- “What limitations have you encountered when using Replication Flows and Transformation Flows in SAP Datasphere with large datasets?”
In a large ETL-style merge into a fact table, what are your top three tactics to improve performance and reduce redo and undo when processing millions of rows?
Asked once · last asked Sep 7 · Scenario · Senior
Why keep data in Parquet instead of JSON or XML?
Asked once · last asked Sep 9 · Concept · Mid-level
How would you transfer 10 TB from a third-party object storage service to a Google Cloud Storage bucket as quickly as possible?
Asked once · last asked Sep 9 · Design · Senior
Which distributed system would you use to process 5 million customer records per day in a data pipeline?
Asked once · last asked Sep 9 · Design · Mid-level
Can a laptop process 5 million records per day, and what factors would determine whether it can?
Asked once · last asked Sep 9 · Scenario · Mid-level
How would you bulk-load large amounts of data into Oracle compared with MongoDB?
Asked once · last asked Sep 9 · Tooling · Mid-level
When extracting data from Salesforce for BigQuery, where would you apply filters and optimizations: at the source, after loading, or both?
Asked once · last asked Sep 10 · Scenario · Mid-level
How would you choose file formats for a data platform using Kafka, object storage, and Spark?
Asked once · last asked Sep 10 · Design · Senior
When would you choose Avro, and when would you choose Parquet?
Asked once · last asked Sep 10 · Concept · Mid-level
When data files are stored in Amazon S3, what are the trade-offs between using Protocol Buffers and having an analytics system read the files directly?
Asked once · last asked Sep 15 · Design · Senior
How do you choose between Delta and Iceberg across the data lifecycle?
Asked once · last asked Sep 16 · Scenario · Senior
What technology would you use to read data across clouds while reducing cost and improving performance?
Asked once · last asked Sep 16 · Scenario · Senior
How would you diagnose and resolve performance issues when moving data from on-premises systems to the cloud?
Asked once · last asked Sep 16 · Scenario · Senior
A data pipeline that took five minutes in a notebook now takes 30 minutes in production. How would you debug it, and what are likely root causes?
Asked once · last asked Sep 18 · Scenario · Senior
If an external API suddenly cuts its rate limit in half, what would you change first in an export process?
Asked once · last asked Sep 18 · Scenario · Senior
How would you identify the bottleneck in a slow data pipeline or analytical query, decide what to change, and verify the improvement?
Asked once · last asked Sep 20 · Behavioral · Senior
Other ways it was asked
- “How did you identify the bottleneck in a slow data pipeline or analytical query, what did you change, and how did you verify the improvement?”
How do you troubleshoot and optimize a pipeline job that suddenly slows down because of a large join?
Asked once · last asked Sep 23 · Scenario · Senior
How would you determine whether a slow data pipeline is caused by increased data volume, Spark performance, Dataproc resources, BigQuery, an upstream API, or data skew?
Asked once · last asked Sep 23 · Scenario · Senior
How would you improve a backend API that takes about a minute to import hundreds of thousands of records from a CSV file?
Asked once · last asked Oct 1 · Scenario · Senior
Other ways it was asked
- “How would you address timeouts when a backend API imports hundreds of thousands of records from a CSV file?”
Why might you choose CSV instead of Parquet for storing data?
Asked once · last asked Oct 7 · Concept · Mid-level
Can a large JSON file be split for parallel processing?
Asked once · last asked Apr 17 · Concept · Mid-level
Other ways it was asked
- “Can a large JSON file be read in parallel by multiple Spark executors?”
What are the benefits of Parquet?
Asked once · last asked Sep 10 · Concept · Junior
What data formats are commonly used in big data, including Parquet?
Asked once · last asked Sep 17 · Concept · Junior
More ETL and Data Pipelines interview questions
- All ETL and Data Pipelines interview questions
- ETL Basics and Data Pipeline Architecture Interview Questions
- Data Ingestion and Transformation Interview Questions
- Dbt Interview Questions
- Data Pipeline Orchestration and Deployment Interview Questions
- Incremental Loading, CDC, and Streaming Interview Questions
- ETL Data Quality and Validation Interview Questions
- Data Pipeline Monitoring and Troubleshooting Interview Questions
- Data Migration Interview Questions
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 ETL and Data Pipelines 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.
307 interviews, 169 candidates, Nov 2025 to Oct 2026. Updated every month with new interviews.
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