Data Manipulation (SQL/Python) Interview Questions
Practice 665 real Data Manipulation (SQL/Python) interview questions for 2026. Data Manipulation (SQL/Python) interview questions cover the core data work hiring teams use to judge day-one competence: translating product metrics into correct queries, handling edge cases (NULLs, late-arriving events, double-counting), writing efficient joins and window functions, and producing readable, reproducible Pandas code. Real interviews with detailed solutions in this category test both correctness and engineering judgment, not just syntax. Expect these questions across analytics and data-engineering loops at companies that weight practical querying heavily — especially Meta, Amazon, and TikTok. Interviewers evaluate problem scoping, test-case thinking, query performance, and clear communication of assumptions. For interview preparation, focus on hands-on practice: timed live SQL exercises, Pandas data-cleaning drills, and walking through tradeoffs when a naive query will be too slow. Practical habits that help in interviews include writing concise, well-commented queries, stating assumptions up front, and verifying results with small, explicit test cases.

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Compute 7-day rolling complaint/order ratio in SQL
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Write complex SQL for streaming funnels
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Set up a Python interview environment
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Write PostgreSQL string-manipulation query
You are given a PostgreSQL table clickstream(session_id TEXT, page TEXT, query TEXT, ts TIMESTAMP). The page column contains full URLs like 'https://s...
Create OHLC Aggregates from Tick Data in Python
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Compute per-coin quarterly amounts and totals
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Compute ads revenue by geography in SQL
You have ad delivery logs for a shop-ads system. Tables ad_impressions - impression_id STRING (PK) - ts TIMESTAMP (UTC) - user_id STRING - shop_id STR...
Compute active ad revenue by creation source
You work on an ads platform and need to report active ad revenue broken down by the ad’s creation source. Tables ads - ad_id BIGINT PK - advertiser_id...
Compute churn with re-subscriptions
You are analyzing a free-month subscription experiment. Assume experiment eligibility has already been computed; every user in the experiment table is...
Compute a Seven-Day Rolling Average
You are analyzing daily product metrics for the Fetch mobile app. Table daily_metric_values | Column | Type | Description | |---|---:|---| | metric_da...
Explain pandas and SQL basics
You are interviewing for a Data Engineer co-op/intern role. Answer the following short technical questions. Python / pandas: 1. What is the difference...
Explain a SQL query result
Given two tables and a specific SQL query, precisely explain the expected result set: which rows are returned, what each column contains, how joins/fi...
Write monthly customer and sales SQL queries
You are analyzing a food-delivery marketplace. Tables Assume the following schema (you may add minor helper CTEs as needed): orders - order_id (BIGINT...
Merge and Concatenate Inconsistent Order Files with Pandas
orders_2023 +----------+-------------+--------+ | order_id | customer_id | amount | +----------+-------------+--------+ | 101 | C001 | 120...
Find top category by video time spent
Pandas required. You are given a DataFrame df with columns: user_id (int), pin_id (int), pin_type (str), category (str or None), time_spent_sec (numer...
Transform clickstream with pandas sessionization
Given a pandas DataFrame events with columns [user_id:int, ts:str ISO8601 or NaT, url:str, server_log_ts:datetime], build 30-minute inactivity session...