Dunnhumby Data Engineer Interview Questions & Answers (2026)
Practice the 48 most asked data engineering questions at Dunnhumby. Covers Spark/Big Data, Python/Coding, General/Other and more.
Key Takeaways
- ✓Why Dunnhumby Tests These Questions
- ✓Top 5 Most Asked Questions at Dunnhumby
- ✓Category Breakdown for Dunnhumby Interviews
- ✓How to Prepare
Why Dunnhumby Tests These Questions
Dunnhumby is known for rigorous data engineering interviews that focus on practical, production-level knowledge. With 48 questions in our vault, the most common category is Spark/Big Data (24 questions).
Difficulty breakdown: 14 easy, 17 medium, 17 hard. Expect system design and optimization questions at senior levels.
Top 5 Most Asked Questions at Dunnhumby
- Q1: What is the difference between repartition and coalesce in Apache Spark?
- Q2: What is the difference between narrow and wide transformations in Apache Spark? Explain with examples.
- Q3: Explain the difference between Spark's map() and flatMap() transformations.
- Q4: How does Spark's Catalyst Optimizer work? Explain its stages.
- Q5: What is the difference between Managed and External tables in Hive/Spark?
Category Breakdown for Dunnhumby Interviews
- Spark/Big Data: 24 questions
- SQL: 14 questions
- General/Other: 7 questions
- Python/Coding: 3 questions
How to Prepare
Focus on Spark/Big Data questions first, as they dominate Dunnhumby's interview pattern. Practice the top-frequency questions below, then move to adjacent categories. For senior roles, expect 1-2 system design rounds.
Reviewed by Aditya Kumar · DataEngPrep Editorial Team
Drafted by the editorial team and signed off by Aditya Kumar, founder and lead editor at DataEngPrep. Questions are sourced from real interviews, initial answers are drafted with AI assistance, and every section is human-edited for technical accuracy, relevance to current FAANG hiring rubrics, and clarity. Articles are reviewed periodically as interview patterns evolve.
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