Data Scientist Data Manipulation (SQL/Python) Interview Questions
The SQL asked of a Data Scientist is metric definition in disguise, and these 567 questions from Meta, Amazon, TikTok, Capital One and DoorDash keep proving it: compute a response rate, split click-through against conversion for an ad campaign, report weekly revenue and order counts for one delivery type, or track adoption and transaction rates across regions. The mechanics stay in a narrow band. Joins across users, events and orders; GROUP BY with HAVING; CASE WHEN to bucket a cohort; DATE_TRUNC down to a weekly grain; ROW_NUMBER and LAG for first-touch attribution and period-over-period change; COALESCE where a LEFT JOIN has left holes. What decides the round is whether your denominator is right, whether the query survives duplicate rows, and whether you noticed that an active user needs defining before it can be counted. Many questions take a pandas answer instead, the same logic written as merge and groupby. 505 open a PostgreSQL console you can run against seeded tables before reading the solution, and 94% are readable without a premium account. Difficulty barely moves: 487 sit at medium and only 13 are rated hard. This is screening territory, with 339 asked in a technical screen.
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