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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user_sessions +---------+------------+------+---------------------+---------------------+ | user_id | session_id | app | start_time | end_ti...
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Orders +----------+-------------+------------+---------+------------------+ | order_id | customer_id | order_date | amount | product_category | +----...
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SALES +----------+----------+------------+------------+----------+---------+ | sale_id | store_id | product_id | sale_date | quantity | revenue | +-...
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sales_data +------------+--------+-----------+----------+------------+---------+ | date | sku_id | unit_sold | revenue | promo_flag | store_id|...
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loan_payments +------------+------------+-------------+-----------+---------+ | loan_id | payment_id | payment_date | amount | status | +------...
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shop_impressions +------------+---------+----------+--------+ | date | shop_id | position | clicks | +------------+---------+----------+--------...
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shipment +-------------+----------+-----------+---------+---------+-------------+-----------+ | shipment_id | order_id | ship_date | carrier | origin ...
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user_events +---------+---------------------+------------+-------+ | user_id | event_time | event_type | page | +---------+-----------------...
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