Senior+ Data Manipulation (SQL/Python) Interview Questions
Senior+ data interviews push SQL and data manipulation well past basic SELECTs. These questions were reported from senior-level interviews, so they cluster around the harder techniques: multi-level window functions, recursive and layered CTEs, cohort and retention logic, and pandas transformations that mirror real analytics work. At the senior+ level, interviewers also expect you to reason about query performance and explain why one approach beats another. Drill these to get fluent in the patterns that separate senior+ data candidates — writing a running total, a month-over-month change, or a cohort query without reaching for a reference. Most questions come with a worked solution so you can compare your query to a clean one.
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Find Every Project with the Most Employees
The interview report explicitly identified the “Project with Most Employees” SQL exercise. Use the following self-contained PostgreSQL schema: `text e...
Debug and harden trial-assignment Python code
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Analyze Pirated Theme Usage
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Find the Highest-Paid Employee in Each Department
The interview report explicitly identified the “Department Highest Salary” SQL exercise. Use the following self-contained PostgreSQL schema: `text dep...
Query departments and top earners
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Query US-based users and most active forums
You are given user and post data for an online forum platform. Tables (schemas) Assume the following tables: users - user_id BIGINT (PK) - home_countr...
Write Queries for Pinterest Engagement Tasks
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Explain Pandas and SQL Basics
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Write SQL for post-trial conversion cohorts
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Set up a Python interview environment
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Compute 30-day power users and 7-day retention
Compute 30-day power users and 7-day retention You are interviewing for a Product/Risk Data Scientist role at a crypto exchange. Assume all timestamps...
Write SQL for influence score and follower growth
You are working on a social product with these tables: Tables / Schemas users - user_id BIGINT (PK) - created_at TIMESTAMP posts - post_id BIGINT (PK)...
Write SQL/Python for ACH fraud analytics
As of today (2025-09-01), use the following schema and tiny samples to answer. Provide SQL for the SQL parts and Python for the Python part. Schema - ...
Design a reproducible data pipeline for modeling
You receive raw clickstream events and a user table. Build a reproducible daily pipeline that outputs user-day features for modeling. It must be idemp...
Compute dasher pay from deliveries
Given a list of delivery events for dashers (e.g., dasherId, pickupTime, dropoffTime, distance, tip, and optional bonuses) and a set of pay rules (e.g...
Compute invalid event percentage by pixel
Context You work on an ads pixel instrumentation platform. Each pixel emits events throughout the day; some events are missing (not observed) and some...
Build a cohort dashboard with Streamlit and SQL
You are given three tables and asked to build a minimal Streamlit app that helps a PM explore weekly cohort retention and ARPU by country. Assume SQL ...
Diagnose zero IE uploads with SQL
On 2025‑09‑01 ('today'), the dashboard shows image uploads for Internet Explorer (IE) dropped to zero. Write SQL to (a) determine if this is a technic...
Process real-time enter/exit events and actives
You receive a real-time stream of events with schema: user_id (str), channel (str), event_type ("enter"|"exit"), ts (UTC ISO timestamp). A user can ‘e...
Write SQL for 30-day activity and D7 retention
You are given two tables. Table: trades - user_id (BIGINT) - trade_time (TIMESTAMP) — timestamp of the trade - volume (NUMERIC) - asset (TEXT) Table: ...