Real interview questions asked at Lumiq. Practice the most frequently asked questions and land your next role.
Lumiq data engineering interviews test your ability across multiple domains. These questions are sourced from real Lumiq interview experiences and sorted by frequency. Practice the ones that matter most. This set leans toward senior-level depth (7 of 12 are tagged hard). Recurring themes are partition, join, and optimization — 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: 2 easy, 3 medium, and 7 hard. The distribution skews toward harder problems, reflecting the depth expected in senior-level interviews.
The most frequently tested areas in this set are partition (8), join (6), optimization (5), sql (4), spark (3), and lakehouse (1). 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.
Explain your cloud-based data pipeline on AWS
Data Security in BFSI - encryption, IAM, auditing
Data Storage and Retrieval Optimization techniques
Spark Coding: Using explode() Function to flatten nested arrays
Data Modeling and Airflow Scheduling - star schema, cron, backfill
Designing scalable data models - explain approach
Kafka Basics - architecture, topics, partitions, producers, consumers, Zookeeper
Query Performance in Redshift - optimization
SQL Problem - multiple table joins and window functions
Data-Related Issues Encountered - handling skewed data
Spark Optimization - broadcast joins, caching, coalescing, predicate pushdown, AQE
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