Real interview questions asked at Nagarro. Practice the most frequently asked questions and land your next role.
Nagarro data engineering interviews test your ability across multiple domains. These questions are sourced from real Nagarro interview experiences and sorted by frequency. Practice the ones that matter most. This set leans toward senior-level depth (11 of 22 are tagged hard). 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 Coforge and Altimetrik, 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 22 curated questions: 2 easy, 9 medium, and 11 hard. The distribution skews toward harder problems, reflecting the depth expected in senior-level interviews.
The most frequently tested areas in this set are partition (16), spark (12), join (11), optimization (9), sql (7), and python (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.
Tell me about yourself and your experience.
What is the difference between groupByKey and reduceByKey in Spark?
What is the difference between narrow and wide transformations in Apache Spark? Explain with examples.
What architecture are you following in your current project, and why?
Handling Large-Scale Data Ingestion in AWS Pipelines
Data Shuffling Causes and Techniques
Graph Databases - explain
Cloud Architecture - explain
Converting SCD0 to SCD3
Facts and Dimension Tables Properties
Features of NoSQL Databases
How to Handle Null in Spark
SCD Implementation in ETL
SQL Query for Best of 3 Marks and Average in a Student Table
Challenges with Spark Jobs and Resolutions
How to Upsert Your Data Daily Using Spark
Monitoring and Orchestrating Spark Jobs
PySpark Code for Broadcast Join and Conditional Aggregation by Location
What is Broadcast Join and Why is It Required?
What is Predicate Pushdown and AQE with Example
What is Shuffle and How to Handle It in Spark
Data Volume in Pipelines and Scalability Solutions
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