Meta data engineer interview guide
Meta data engineering interviews lean heavily on product sense: you model data for a product feature, write SQL to compute its metrics and explain how the pipeline keeps those metrics fresh.
Interview rounds
SQL round
Fast, accurate queries on product tables — joins, window functions, daily active users, retention and funnels.
Practice SQL problems →Python / coding round
Short data-manipulation problems with lists, dicts and strings, solved without heavy libraries.
Practice Python problems →Data modeling round
Design tables for a product feature, then show how they answer the key business metrics.
Practice data modeling →Pipeline / system design round
Design a batch or streaming pipeline that feeds dashboards, with attention to data quality and backfills.
Practice system design →Sample Meta interview questions
- Compute daily active users and a rolling 7-day active user count.
- Find users who converted at each step of a signup funnel.
- Model a social feature (posts, likes, follows) and define its north-star metric.
Tips to pass
- Talk through the metric before writing SQL.
- Practice window functions until they are automatic.
- Always mention how you would validate the data.
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