Real interview questions on Snowflake-warehouses, materialized views, clustering, time travel, and cloud data platform architecture.
Snowflake has become a dominant cloud data warehouse. These questions cover Snowflake-specific concepts: virtual warehouses, compute scaling, materialized views vs streams, clustering keys, time travel, data sharing, and query optimization. Prepare for Snowflake-focused roles at enterprises and tech companies.
This collection contains 38 curated questions: 10 easy, 11 medium, and 17 hard. The distribution skews toward harder problems, reflecting the depth expected in senior-level interviews.
The most frequently tested areas in this set are snowflake (22), partition (19), spark (17), bigquery (15), sql (13), and optimization (13). 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 is the difference between repartition and coalesce in Apache Spark?
CDC During Migration - explain approaches for real-time Change Data Capture
Explain the differences between a Data Lake and a Data Warehouse.
Explain the differences between Data Warehouse, Data Lake, and Delta Lake
Can you explain the difference between OLTP and OLAP?
What is a Common Table Expression (CTE), and when would you use it?
When would you choose a Snowflake schema over a Star schema?
Describe the data pipeline architecture you've worked with.
Difference Between Internal and External Tables in BigQuery
Have you worked on Data Warehousing projects?
Prioritize Spark optimizations by impact and effort. Discuss partitioning strategy, caching policy, join selection, shuffle reduction, and when each becomes a scalability or cost bottleneck.
Retrieve the most recent sale_timestamp for each product (Latest Transaction).
Walk through the three AQE features in Spark 3.x (coalesce, join switch, skew join)—how they operate at shuffle boundaries, which configs enable them, and what happens when AQE cannot help.
What is a CTE (Common Table Expression)? What are its uses?
What is Adaptive Query Execution (AQE) in Spark 3.x, and how does it improve performance?
What is Snowflake's architecture, and why is it unique?
What is the difference between a view and a materialized view?
What is the difference between Managed and External Tables in Databricks?
What is the difference between OLTP and OLAP?
What is the difference between OLTP and OLAP?
What is the most difficult task you've ever worked on?
Why should we hire you for this role?
Approaches to handling multiple tasks within a sprint?
Briefly introduce yourself and walk us through your journey as a Data Engineer so far.
Broadcast Joins and Shuffle Merge Joins?
Business Role of Data Pipeline
Can Presto work with Near Real-Time Data (Streaming Data Source)?
Can you describe a situation where you had to work with a difficult stakeholder? How did you manage the situation and what was the outcome?
Challenges faced in translating requirements into technical solutions?
Compare Native vs Cloud Database Systems.
Compare OLTP and OLAP systems in the context of financial transactions.
Compare PostgreSQL vs Snowflake. How do they handle duplicate record errors?
Compare Redshift, BigQuery, and Snowflake in terms of cost, performance, and scalability.
Compare the star schema and snowflake schema. Which one would you use for reporting at Swiggy, and why?
Could you describe a specific cost optimization strategy you implemented in the cloud and its results?
Data Warehouse Design from scratch
Demonstrate system design principles applied to BI solutions.
Describe an end-to-end data pipeline project you worked on, highlighting your role and the technologies used.
Snowflake automatically stores table data in compressed, columnar units of roughly 50–500 MB called micro-partitions. For each one it keeps metadata (min/max values, distinct counts) so the optimizer can prune partitions that can't match a query's filters. You don't create or manage them manually — understanding that pruning is metadata-driven is the key interview point.
Only on large tables (typically multi-terabyte) where queries repeatedly filter or join on a column whose values aren't naturally well-ordered by load time, causing poor natural pruning. A clustering key reorganizes micro-partitions around that column. It adds ongoing reclustering cost, so for small or already-well-ordered tables it's usually unnecessary.
QUALIFY filters the result of a window function in the same query — the way WHERE filters rows and HAVING filters groups. It lets you write, for example, QUALIFY ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY ts DESC) = 1 to keep the latest row per user without a subquery. It's a Snowflake/BigQuery favorite for deduplication.
Storage lives in cloud object storage and is billed independently, while compute runs on virtual warehouses you can size and start/stop on demand. Multiple warehouses can read the same data concurrently without contention. This separation is why Snowflake scales compute elastically and is a core architecture talking point in interviews.
Time Travel lets you query, clone, or restore table data as it existed at a past point (up to 1 day on standard, up to 90 days on enterprise) using AT/BEFORE clauses. It's used to recover from accidental deletes/updates and to build reproducible pipelines. Pair it with zero-copy cloning to create instant, cheap test copies.
The Data Engineering Interview Answer Vault bundles 750+ reviewed answers into 7 focused PDF volumes — SQL, Spark, Python, System Design, Cloud, Behavioral, and Data Modeling. Study on any device, no subscription required.
800+ hands-on courses — Grokking System Design, Coding Patterns, and AI mock interviews for your DE loop.
Turn any topic or your own notes into an interactive, personalized course in 60 seconds.
The book that gets data engineers through system-design rounds. Essential reading.
Some links below are affiliate links. If you buy through them we may earn a small commission at no extra cost to you — it helps keep DataEngPrep free.
Reading answers is step one. Get instant AI feedback on your answers, run mock interviews, and track readiness — built specifically for data engineering interviews.