Real interview questions asked at Wipro. Practice the most frequently asked questions and land your next role.
Wipro data engineering interviews test your ability across multiple domains. These questions are sourced from real Wipro interview experiences and sorted by frequency. Practice the ones that matter most. This set leans toward fundamentals — 14 easy, 9 medium, and 5 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 EPAM and Fragma Data Systems, 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 28 curated questions: 14 easy, 9 medium, and 5 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 (4), join (4), window (3), etl (2), and optimization (2). 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?
Demonstrate the difference between DENSE_RANK() and RANK()
Write a query to find the top three highest-paid employees in each department using window functions.
Can you provide an example of a time when you went above and beyond for a project?
How do you handle feedback and criticism?
How do you prioritize tasks when managing multiple projects simultaneously?
Suppose two teams have conflicting requirements for the same data. How would you manage the situation?
What challenges did you face with data integration and how did you resolve them?
What steps do you take to ensure effective communication in a remote team?
What would you do if a stakeholder requested a last-minute change to a project deliverable?
Why are you interested in this role?
Explain the process of setting up an ETL pipeline using AWS services.
How do you manage data storage in AWS?
How would you design a data pipeline using AWS Glue, S3, and Redshift?
How would you optimize cost when using AWS for large-scale data processing?
What is the difference between S3 and EFS? When would you use each?
Which cloud services (AWS or others) did you leverage in your project? Why?
Are you open to relocation or a hybrid work model?
How would you handle large datasets in a distributed computing environment?
How would you handle massive data ingestion in a cloud environment?
How would you implement a data quality framework using AWS services?
How would you optimize a slow-running SQL query?
What benefits or perks are most important to you?
Can you describe a project where you handled large volumes of data?
Demonstrate how to use a LEFT JOIN to combine data from two tables and handle null values.
How do you handle negotiations if your expected salary isn't met?
How would you retrieve the first and last order for each customer from a sales table?
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