Data modeling mock interviews with an AI interviewer
Data modeling interviews ask you to turn a business into tables: model the trips and payments of a ride-sharing app, design the warehouse for an online store, decide how to keep history when a customer changes address. There's no single right answer, so you're graded on whether your choice of grain, keys and structure makes the important questions easy and correct to answer.
PrepLinx gives you a modeling problem and a whiteboard for your schema. The AI interviewer reads your diagram and asks what a real interviewer would: what's the grain of this fact table, how does this query perform, what happens when this attribute changes.
What this round tests
Modeling concepts
Entities, relationships, keys and cardinality.
Normalization
Knowing when to normalize and when to denormalize.
Dimensional modeling
Star and snowflake schemas, facts and dimensions.
Warehouse design
Layers, grain and history tracking.
Performance
Partitioning, clustering, and how the model serves its query patterns.
Communication
Explaining your model and its trade-offs clearly.
Practice questions
Examples of the questions and follow-ups you can expect. Practice saying your answers out loud, not just reading them.
- Model the data for a ride-sharing app's trips, drivers and payments.
- What's the grain of your orders fact table, and why?
- A customer changes their address. How does your model keep the history?
- Star or snowflake schema for this use case?
- Analysts mostly query the last 30 days by region. How would you partition this table?
- Design the schema for a subscription business's billing data.
How a PrepLinx session works
- 1
Choose the round
Pick the role, the interview type and a difficulty (easy, medium or hard). Optionally add a job profile with your resume and the job description, so the questions relate to that job.
- 2
Interview out loud
The AI interviewer speaks, listens to your answers and asks follow-ups. It doesn't give hints or say whether you're right, just like the real thing. The session runs up to 60 minutes.
- 3
Get scored feedback
A report with a 0–10 score for each dimension the round is graded on, strengths quoted from your own words, weaknesses, concrete improvements and an overall hiring signal, plus the full transcript.
How to prepare
Start from the questions
Ask which questions the model must answer. They decide the grain and the dimensions.
Declare the grain
Say what one row of each fact table represents before adding columns.
Know slowly changing dimensions
Be ready to compare type 1 and type 2 and choose one for a given attribute.
Draw it
A clear diagram with the keys marked makes the conversation easier for both of you.
Tie the design to query patterns
Explain how partitioning and denormalization serve the queries you expect.
Frequently asked questions
Do I draw the schema?
Yes, on the whiteboard. Ask the interviewer to look at it, and it reads your diagram and asks about it.
Does this cover dimensional modeling?
Yes. Star and snowflake schemas, fact and dimension design, and warehouse modeling are core topics, alongside normalization and entity-relationship modeling.
Who is this for?
Data engineers and analytics engineers, and anyone whose interviews include schema or warehouse design.
How long is a session?
Up to 60 minutes.
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Practice your next data modeling interview
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