System Design questions from Swiggy data engineering interviews.
These system design questions are sourced from Swiggy data engineering interviews. Each includes an expert-level answer. This set leans toward senior-level depth (11 of 13 are tagged hard). Recurring themes are join, partition, and spark — these patterns appear most often in real interviews and reward the deepest preparation. Many of these questions also surface at Expedia, so the preparation transfers across companies. Average answer is around 2 minutes of reading — plan roughly 1 hour to work through the full set thoughtfully.
This collection contains 13 curated questions: 0 easy, 2 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 join (12), partition (12), spark (11), optimization (6), window (4), and snowflake (2). Focusing on these topics will give you the highest return on your preparation time.
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.
Explain the trade-offs between batch and real-time data processing. Provide examples of when each is appropriate.
Describe a scenario where you had to optimize a slow-running data pipeline.
Design a data warehouse schema to track orders, customers, delivery partners, and payments.
Design a logging and monitoring solution for a mission-critical data pipeline.
Design a system to handle 1M daily transactions with real-time analytics for Swiggy.
Discuss trade-offs between serverless and traditional cloud data architectures.
Explain how you would design a pipeline for streaming real-time order status updates.
How do you ensure data quality in an automated pipeline?
How do you ensure the scalability of a data pipeline handling rapidly growing data volumes?
How do you handle schema evolution in a system with multiple data sources and consumers?
How would you handle late-arriving data in a real-time stream processing pipeline?
How would you handle schema changes in a production ETL pipeline?
How would you use monitoring tools to detect and resolve pipeline failures proactively?
Get full access to 1,800+ expert answers, AI mock interviews, and personalized progress tracking.