Real interview questions asked at NAB. Practice the most frequently asked questions and land your next role.
NAB data engineering interviews test your ability across multiple domains. These questions are sourced from real NAB interview experiences and sorted by frequency. Practice the ones that matter most. This set leans toward the medium-difficulty band most real interviews actually live in (5 of 12). Recurring themes are sql, join, and python — these patterns appear most often in real interviews and reward the deepest preparation. Many of these questions also surface at Chryselys and FedEx Dataworks, so the preparation transfers across companies. Average answer is around 1 minute of reading — plan roughly 1 hour to work through the full set thoughtfully.
This collection contains 12 curated questions: 3 easy, 5 medium, and 4 hard. The distribution skews toward harder problems, reflecting the depth expected in senior-level interviews.
The most frequently tested areas in this set are sql (5), join (4), python (3), lakehouse (2), snowflake (2), and etl (2). 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.
Docker - purpose and handling dependencies
Libraries for Data Wrangling
Garbage Collector in Python - explain
GeoPandas - definition and features
Multiprocessing in Python - explain with example
Stuff Function for XML Usages
Indexing: When to Use and Avoid
Materialized View - explain and use cases
Normalization vs Denormalization
Stored Procedure Optimization
Databricks - platform, use cases
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