Interview preparation
Data modeling interview questions for Data Engineers
Every question starts with a direct answer you could say out loud, then explains why, when and what goes wrong. Tick questions off as you practise.
How to use this page
This page collects 8 Data modeling interview questions answered in depth (6 medium and 2 hard), ordered from the basics to the harder design and troubleshooting questions. They include “Design a star schema for a business process. What is the grain”, “Kimball vs Inmon vs Data Vault: which would you choose for a new warehouse”, “One big table or a star schema: how do you decide” and “Surrogate keys vs natural keys: which should a dimension use, and why”.
Each answer opens with a short version you could say out loud in an interview, then explains why it works, shows a worked example, and lists the mistakes and follow-up questions interviewers use. If a topic is new to you, learn it in the Data modeling course first. Tick each question off as you practise; your progress is saved in this browser.
All Data modeling questions
| # | Question | Difficulty | Type | Done |
|---|---|---|---|---|
| 1 | Design a star schema for a business process. What is the grain? | Medium | architecture, scenario | |
| 2 | Kimball vs Inmon vs Data Vault: which would you choose for a new warehouse? | Medium | architecture, conceptual | |
| 3 | One big table or a star schema: how do you decide? | Medium | architecture, scenario | |
| 4 | Surrogate keys vs natural keys: which should a dimension use, and why? | Medium | conceptual, scenario | |
| 5 | Transaction, periodic snapshot or accumulating snapshot: which fact table would you build? | Medium | conceptual, scenario | |
| 6 | What are conformed and role-playing dimensions, and how do you use them? | Medium | conceptual, coding | |
| 7 | How do you implement an SCD Type 2 dimension and query it with as-of joins? | Hard | coding, conceptual | |
| 8 | How do you model late-arriving dimensions and late-arriving facts in a star schema? | Hard | scenario, coding |
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Concept review
- LessonAdvancedBridge Tables, Many-to-Many Relationships and Hierarchies
- LessonBeginnerData Warehousing for Data Engineers
- LessonIntermediateDimension Tables: Keys, Conformed, Junk, Degenerate and Role-Playing Dimensions
- LessonIntermediateFact Tables: Measures, Snapshots and Late-Arriving Facts
- LessonBeginnerData Lake vs Data Warehouse vs Lakehouse
- LessonAdvancedData Modeling Methodologies: Kimball, Inmon, Data Vault, Anchor and Medallion
- LessonAdvancedModern Data Modeling: Streaming Models, Semantic Layers and dbt
- LessonBeginnerNormalization, Denormalization and Wide Tables
- LessonIntermediatePartitioning, Clustering and Data Layout
- LessonIntermediateSlowly Changing Dimensions: Types 0 to 6
- LessonBeginnerStar, Snowflake and Galaxy Schemas and the Grain
Continue preparing
- CourseData modeling courseOrdered lessons, projects and a cheat sheet.
- InterviewAll interview questionsEvery question across all topics in one filterable list.
- System designCase studiesPractise full data platform designs.
- CompaniesCompany preparationGuides with attributed sources and practice questions.