Infosys Data Engineer Interview Questions & Answers (2026)
Practice the 39 most asked data engineering questions at Infosys. Covers Spark/Big Data, Python/Coding, Cloud/Tools and more.
Key Takeaways
- ✓Why Infosys Tests These Questions
- ✓Top 5 Most Asked Questions at Infosys
- ✓Category Breakdown for Infosys Interviews
- ✓How to Prepare
Why Infosys Tests These Questions
Infosys is known for rigorous data engineering interviews that focus on practical, production-level knowledge. With 39 questions in our vault, the most common category is Spark/Big Data (19 questions).
Difficulty breakdown: 17 easy, 14 medium, 8 hard. Expect system design and optimization questions at senior levels.
Top 5 Most Asked Questions at Infosys
- Q1: What is the difference between SparkSession and SparkContext in Spark?
- Q2: What are traits in Scala, and how are they different from classes?
- Q3: How do you handle data security and compliance in a cloud environment?
- Q4: How would you read data from a web API? What steps would you follow after reading the data?
- Q5: Architecturally, how would you justify or challenge Hadoop vs. a cloud-native data lake (S3 + EMR/Databricks) for a greenfield enterprise data platform? Discuss scalability ceilings, cost model trade-offs, and operational complexity.
Category Breakdown for Infosys Interviews
- Spark/Big Data: 19 questions
- Python/Coding: 12 questions
- General/Other: 2 questions
- System Design/Architecture: 2 questions
- SQL: 2 questions
- Cloud/Tools: 1 questions
- Behavioral: 1 questions
How to Prepare
Focus on Spark/Big Data questions first, as they dominate Infosys's interview pattern. Practice the top-frequency questions below, then move to adjacent categories. For senior roles, expect 1-2 system design rounds.
Reviewed by Aditya Kumar · DataEngPrep Editorial Team
Drafted by the editorial team and signed off by Aditya Kumar, founder and lead editor at DataEngPrep. Questions are sourced from real interviews, initial answers are drafted with AI assistance, and every section is human-edited for technical accuracy, relevance to current FAANG hiring rubrics, and clarity. Articles are reviewed periodically as interview patterns evolve.
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