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Swiggy Data Engineer Interview Questions & Answers (2026)
Practice the 66 most asked data engineering questions at Swiggy. Covers SQL, Spark/Big Data, Python/Coding and more.
Why Swiggy Tests These Questions
Swiggy is known for rigorous data engineering interviews that focus on practical, production-level knowledge. With 66 questions in our vault, the most common category is SQL (21 questions).
Difficulty breakdown: 19 easy, 21 medium, 26 hard. Expect system design and optimization questions at senior levels.
Top 5 Most Asked Questions at Swiggy
- **Q1**: Describe a scenario where partitioning and bucketing would improve query performance.
- **Q2**: How do you handle late-arriving data in Spark Structured Streaming?
- **Q3**: What is the small-file problem in Spark, and how do you solve it?
- **Q4**: How do you optimize Spark jobs for better performance? Mention at least 5 techniques.
- **Q5**: What are decorators in Python, and how do they work?
Category Breakdown for Swiggy Interviews
- **SQL**: 21 questions
- **Behavioral**: 14 questions
- **System Design/Architecture**: 13 questions
- **Spark/Big Data**: 11 questions
- **Python/Coding**: 4 questions
- **General/Other**: 2 questions
- **Cloud/Tools**: 1 questions
How to Prepare
Focus on SQL questions first, as they dominate Swiggy's interview pattern. Practice the top-frequency questions below, then move to adjacent categories. For senior roles, expect 1-2 system design rounds.
Practice These Questions
mediumDescribe a scenario where partitioning and bucketing would improve query performance.→hardHow do you handle late-arriving data in Spark Structured Streaming?→hardWhat is the small-file problem in Spark, and how do you solve it?→hardHow do you optimize Spark jobs for better performance? Mention at least 5 techniques.→easyWhat are decorators in Python, and how do they work?→
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