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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Write SQL for library analytics
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user_events +----------+------------+---------------------+-------------+ | user_id | event_type | event_timestamp | product_id | +----------+--...
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video_calls | call_id | user_id | start_time | end_time | |---------|---------|----------------------|----------------------| | ...
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following_behavior +------------+---------+-----------+---------------+ | date | user_id | hashtag_id| hashtag_source| +------------+---------+-...
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SELLER_STATUS +-----------+------------+-----------+ | seller_id | date | status | +-----------+------------+-----------+ | 1 |2019-0...
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orders +----------+---------+------------+----------+--------------+-------------------+ | order_id | cust_id | order_date | product | order_amount |...
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INFO_STREAM_VIEWS +---------+-----------+--------------+----------+------------+ | post_id | viewer_id | relationship | duration | ds | +-----...
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info_stream_views +----------+-----------+--------------+----------+------------+ | post_id | viewer_id | relationship | duration | ds | +---...
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SHOP_VISIBILITY +----------+---------+------------+------------+-------------+--------------+ | user_id | shop_id | event_date | is_visible | signup_...
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Weather +------------+------------+---------+ | country | date | weather | +------------+------------+---------+ | Spain | 2023-07-01 | ...
Sample and Simulate Price Adjustments in R with dplyr
Products +----+-----------+-------+ | id | product | price | | 1 | phone | 500 | | 2 | tablet | 300 | | 3 | laptop | 1000 | | 4 |...
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transactions +-----------+----------+------------+------------+ | user_id | order_id | product_id | order_time | +-----------+----------+-----------...
Compute window averages and merge intervals
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Calculate cost from orders with SQL
You have two tables: orders(order_id INT, user_id INT, order_date DATE, quantity INT, unit_price DECIMAL, coupon_code VARCHAR) and coupons(code VARCHA...
Implement 2D convolution using NumPy slicing
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Solve library SQL and Python tasks
You are given a library domain. Assume these tables: - books(book_id, author_id, title) - authors(author_id, name) - copies(copy_id, book_id, conditio...