Warehouse modelling, data-quality contracts and a query-performance investigation on a very large table.
1. Take me through how you'd model a warehouse layer for an e-commerce business whose analysts constantly ask new questions.
~3 minChecks modelling fundamentals and consumer empathy.
2. A daily pipeline silently produced 30% fewer rows for a week before anyone noticed. How do you prevent the next one?
~3 minData-quality and observability thinking.
3. When would you choose streaming over batch, and when is streaming the wrong answer?
~3 minTests honest trade-off reasoning instead of hype.
4. A key query on a 4-billion-row table takes nine minutes. What do you look at?
~3 minWarehouse performance depth.
Warm-up & context · 4 min
Interviewer intro, your background, how the session runs.
Core questions · 24 min
Role-specific questions with live follow-ups based on your answers.
Deep dive · 8 min
One topic explored to the edge of your experience.
Your questions & wrap · 4 min
Close the loop and hand over to feedback generation.
Design a near-real-time pipeline, argue when streaming is the wrong answer, and make silent failures impossible.
A hands-on platform interview covering pipeline design, infrastructure as code and how you behave in the first minutes of an incident.
API design, testing strategy and performance debugging, grounded in code you have actually shipped.