Data Manipulation (SQL/Python) Interview Questions
Practice 653 real Data Manipulation (SQL/Python) interview questions for 2026. Covers companies like Meta, Amazon, TikTok, DoorDash, and Capital One. Real questions from actual interviews with detailed solutions — designed for focused interview preparation for data analysts, data scientists, and data engineers who must move fluidly between SQL and Python during live screens and take-home tasks. These questions emphasize practical skills: writing correct, efficient SQL (joins, GROUP BY, window functions, CTEs, NULL handling, and performance-aware predicates) and idiomatic Python/Pandas solutions (vectorized transforms, merges, reshaping, datetime handling, and robust data-cleaning). Interviewers evaluate correctness, edge-case reasoning, runtime and memory tradeoffs, reproducibility, and clear communication of assumptions. Expect timed whiteboard-style queries, pair-programming in a shared editor, and take-home notebooks. To prepare, practice translating SQL ↔ Pandas, explain results aloud, time-box exercises, test edge cases, and review common pitfalls such as NULL semantics, grouping logic, off-by-one errors, and inefficient joins.
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Aggregate D1 retention cohorts in SQL
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Write complex SQL on DoorDash data
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Compute Moderation View Metrics
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Analyze spend cohort and source shifts
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Find high-value crypto users and top CTR
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Identify false MySQL foreign key statement
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