Real interview questions asked at Nihilent. Practice the most frequently asked questions and land your next role.
Nihilent data engineering interviews test your ability across multiple domains. These questions are sourced from real Nihilent interview experiences and sorted by frequency. Practice the ones that matter most. This set leans toward fundamentals — 13 easy, 6 medium, and 11 hard questions. Recurring themes are spark, partition, and join — these patterns appear most often in real interviews and reward the deepest preparation. Many of these questions also surface at FedEx Dataworks and Datametica, 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 30 curated questions: 13 easy, 6 medium, and 11 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 spark (13), partition (11), join (7), optimization (5), sql (4), and etl (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 Repartition and Coalesce. When would you use each?
Explain the types of triggers in ADF, including schedule, tumbling window, and event-based triggers.
Joins and window functions - INNER, LEFT, RIGHT, FULL OUTER, ROW_NUMBER(), RANK(), DENSE_RANK()
Can you explain the architecture of Apache Spark and its components?
Provide a detailed walkthrough of your career journey
Share examples of successful stakeholder communication
Difference between pipelines and data flows in ADF
Fabric dataflows vs. ADF dataflows
Fabric pipelines vs. ADF pipelines
Running multiple notebooks - dbutils.notebook.run()
Types of Integration Runtimes (IR) - self-hosted, Azure, SSIS
Unity Catalog - role in managing and securing data
Agile Methodologies - sprint planning, standups, retrospectives
Explain your roles and responsibilities in your current project
Highlight the tools and technologies you've used in your current project
Lakehouse vs. Warehouse
Share your journey as a Data Engineer
What role does data lineage play in your current project?
Explain techniques for ensuring data quality in cross-functional team scenarios
Python libraries - Pandas, NumPy, Matplotlib for data processing
Optimization techniques - partitioning, caching, broadcast joins, bucketing
Removing duplicates - ROW_NUMBER() or DISTINCT
Serverless vs. Dedicated SQL pools
Write a query for second-highest salary using LIMIT, OFFSET, or ROW_NUMBER()
Accumulators - use as shared variable for write-only operations
Broadcast join - how it optimizes joins
Databricks notebooks vs. Fabric notebooks - differences
Schema evolution - techniques for handling schema changes in PySpark
Writing Excel sheets to Delta tables in Databricks
Discuss designing a data pipeline for a specific use case
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