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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Forecast Next Year's Revenue Using YoY% Analysis
ad_revenue +------------+---------+ | date | revenue | +------------+---------+ | 2023-01-01 | 1000 | | 2023-01-02 | 1200 | | 2024-01-01 |...
Compute Total Spent in 2023 Excluding Refunds
transactions +----------------+---------+-------------+--------+------------------+ | transaction_id | user_id | merchant_id | amount | transaction_da...
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Calls callerid | recipientid | ds | call_id | duration 1001 | 2001 | 2023-02-20| 555 | 180 2001 | 3001 | 2023-02-20| ...
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titanic_passengers +--------------+--------+--------+-----+----------+ | passenger_id | pclass | sex | age | survived | +--------------+--------+--...
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impressions +---------+---------+---------------------+-----------+ | user_id | ad_id | event_time | platform | +---------+---------+-----...
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Orders | order_id | customer_id | order_date | amount | |----------|-------------|------------|--------| | 1 | 101 | 2023-01-05 | 120.5...
Analyze User Engagement and Spammer Read-Rate in SQL
messages +------------+------------+-------------+--------------+-----------+ | sender_id | receiver_id| message_id | sent_date | read_date | +--...
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Transactions +----------------+---------+--------+----------+---------------------+ | transaction_id | user_id | amount | status | timestamp ...
Solve SQL and Python Data Manipulation Tasks
UserEvents +----+---------+------------+------------+ | id | user_id | event_type | event_date | +----+---------+------------+------------+ | 1 | 101...
Calculate Average Rate and Cumulative Jobs by State
jobs +--------+---------+-----------+------------+----------------+-------+----------------+---------------+ | job_id | load_id | driver_id | status ...
Select 10% Users Randomly from User Table
users +----+----------+-------------+ | id | username | signup_date | +----+----------+-------------+ | 1 | alice | 2023-01-02 | | 2 | bob ...
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events +----+---------+------------+-------+---------------------+ | id | user_id | event_type | value | timestamp | +----+---------+-------...
Calculate Monthly Conversion Rate and Average Order Value
page_views +-----------+---------+---------------------+ | user_id | page_id | view_timestamp | +-----------+---------+---------------------+ |...
Analyze Top Call Initiators and Active French Video Callers
calls +---------+-----------+-------------+---------------------+---------+-----------+ | call_id | caller_id | receiver_id | call_start_time | co...
Analyze Oculus App Engagement with SQL Queries
AppUsage +---------+--------+-----------+--------------+------------+ | user_id | app_id | category | minutes_spent| usage_date | +---------+--------...
List Transactions During Active 'Gold' Membership Period
customer_profile +-------------+----------------+--------------------+-------------------+ | customer_id | membership_type| membership_start_date| mem...
Explain MySQL to MS SQL Server query syntax differences.
transactions | id | user_id | amount | txn_date | |----|---------|--------|----------| | 1 | 1001 | 25.00 | 2023-07-01 | | 2 | 1002 | 40.00 ...
Merge and Concatenate Inconsistent Order Files with Pandas
orders_2023 +----------+-------------+--------+ | order_id | customer_id | amount | +----------+-------------+--------+ | 101 | C001 | 120...
Clean and Summarize User Purchase Data Efficiently
transactions +-----------+---------------------+-----------+--------+ | user_id | txn_timestamp | txn_value | txn_id | +-----------+----------...
Calculate User Revenue and Session Duration in Python
events +---------+------------+---------+---------------------+ | user_id | event_type | revenue | timestamp | +---------+------------+-----...