The most frequently asked lakehouse questions in data engineering interviews.
Master lakehouse for your next data engineering interview. These questions cover core concepts, advanced patterns, and real-world scenarios that interviewers test. This set leans toward fundamentals — 12 easy, 2 medium, and 9 hard questions. Recurring themes are lakehouse, spark, and sql — these patterns appear most often in real interviews and reward the deepest preparation. These questions have been reported across 24 companies including NAB and Fragma Data Systems. Average answer is around 1 minute of reading — plan roughly 1 hour to work through the full set thoughtfully.
This collection contains 23 curated questions: 12 easy, 2 medium, and 9 hard. There's a strong foundation of fundamentals-focused questions — ideal for building confidence before tackling advanced topics.
The most frequently tested areas in this set are lakehouse (23), spark (10), sql (8), partition (5), snowflake (3), and bigquery (3). 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.
Explain the differences between a Data Lake and a Data Warehouse.
What is the most difficult task you've ever worked on?
How do you keep yourself updated with new data engineering trends?
What storage format would you choose for analytics-heavy workloads and why?
What's the biggest technical challenge Moonfare faces in handling data?
Why did you choose a particular data storage solution?
Data Lakehouse architecture in Azure?
Describe your experience with cloud platforms like AWS, Azure, or GCP
Explain the differences between Azure SQL Database, Azure SQL Managed Instance, and Azure Synapse.
Synapse Analytics Features and Use Cases?
How do you keep up with the latest trends or tools in data engineering?
Lakehouse vs. Warehouse
What specifically attracts you to Puma as a company?
What is the difference between Data Lakehouse, Delta Lake, and a Data Warehouse?
What technologies are you most comfortable with?
Can Presto work with Near Real-Time Data (Streaming Data Source)?
Can you give a use case where Delta Live Tables would be ideal?
Databricks - platform, use cases
Databricks notebooks vs. Fabric notebooks - differences
Delta Lake: ACID compliance, time travel, streaming support
Delta vs Parquet - explain
What limitations do you face when using Delta Tables in a multi-cloud environment?
Design a data pipeline from end to end - describe how data would be ingested, processed, stored, and queried.
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