Real interview questions asked at EPAM. Practice the most frequently asked questions and land your next role.
EPAM data engineering interviews test your ability across multiple domains. These questions are sourced from real EPAM interview experiences and sorted by frequency. Practice the ones that matter most. This set leans toward fundamentals — 15 easy, 10 medium, and 13 hard questions. Recurring themes are partition, spark, and join — these patterns appear most often in real interviews and reward the deepest preparation. Many of these questions also surface at Accenture and Yash Technologies, so the preparation transfers across companies. Average answer is around 1 minute of reading — plan roughly 1 hour to work through the full set thoughtfully.
This collection contains 38 curated questions: 15 easy, 10 medium, and 13 hard. There's a strong foundation of fundamentals-focused questions — ideal for building confidence before tackling advanced topics.
The most frequently tested areas in this set are partition (12), spark (12), join (11), sql (7), optimization (6), and etl (5). Focusing on these topics will give you the highest return on your preparation time.
Start with the easy questions to warm up and solidify fundamentals. Medium-difficulty questions form the bulk of real interviews — spend the most time here and practice explaining your reasoning out loud. Hard questions often appear in senior and staff-level rounds; attempt them after you're comfortable with the basics. For each question, try answering before revealing the solution. Use our AI Mock Interview to simulate real interview conditions and get instant feedback on your responses.
What are your salary expectations for this role?
Where do you see yourself in your career five years from now?
Briefly introduce yourself and walk us through your journey as a Data Engineer so far.
Can you explain the difference between OLTP and OLAP?
Explain the concept of ACID properties in the context of databases.
Explain the difference between INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.
How do you handle NULL values in SQL? Mention functions like COALESCE and NULLIF.
What is a Common Table Expression (CTE), and when would you use it?
What is the difference between a primary key and a unique key?
What is the difference between WHERE and HAVING clauses in SQL?
How do you handle conflicts within a team? Provide an example.
How do you handle data security and compliance in a cloud environment?
Why do you want to join EPAM?
Describe a scenario where AWS Data Pipeline is preferred over Glue. Why?
Describe how you would use AWS Glue to schedule and manage Spark jobs.
Discuss the key differences between AWS Glue, Lambda, and Data Pipeline for orchestrating data workflows.
Explain how AWS Glue interacts with on-premises SQL databases to extract data efficiently.
Explain when you would use Glue instead of Lambda for a data ingestion use case.
In AWS Data Pipeline, how would you design a process to copy only recently modified files from one S3 bucket to another?
Describe your preferred work environment and collaboration style.
How do you handle large data transfers with minimal downtime?
How do you secure API requests in this setup?
Walk me through your resume. What are the key highlights that align with this role?
What are you seeking in your next role that your current position does not offer?
What are your expectations for this role?
What do you think differentiates EPAM from other consulting firms in the data engineering space?
Describe a recent project where you used AWS services extensively. What was your role, and what challenges did you face?
Describe the process for migrating data from an on-premises SQL database to AWS. What services and strategies would you use?
Discuss a project where you significantly impacted performance or cost optimization.
Explain how you would implement partitioning and bucketing for data stored in S3 to improve query performance.
What challenges arise with duplicate records, and how do you address them?
What is your preferred location, and how soon can you join?
When would you choose partitioning over bucketing, or vice versa?
Describe how you would optimize slow-running Spark jobs in a distributed environment.
Explain your approach to monitoring and logging Spark jobs in AWS. What tools would you use to identify performance bottlenecks?
How do you implement incremental updates in a data lake using AWS services and Spark?
Design a data pipeline to ingest and process data from multiple sources (e.g., S3, Kinesis) to Redshift using Spark.
How would you fetch data from an external API, and what AWS services would you use to build a scalable data pipeline?
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