DoorDash Data Scientist Data Manipulation (SQL/Python) Interview Questions
Practice 18 real Data Manipulation (SQL/Python) interview questions for Data Scientist roles at DoorDash.
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Calculate the Monthly Share of High-Frequency Customers
Calculate the Monthly Share of High-Frequency Customers Write one PostgreSQL SELECT statement or CTE query. Do not create, alter, or modify tables. Sc...
Measure Customers Ordering from Bottom-Quartile Restaurants
Measure Customers Ordering from Bottom-Quartile Restaurants Write one PostgreSQL SELECT statement or CTE query. Do not create, alter, or modify tables...
Find Monthly Top Customers Excluding High-Frequency Users
Find Monthly Top Customers Excluding High-Frequency Users Write one PostgreSQL SELECT statement or CTE query. Do not create, alter, or modify tables. ...
Calculate Monthly Restaurant Sales Growth
Calculate Monthly Restaurant Sales Growth Write one PostgreSQL SELECT statement or CTE query. Do not create, alter, or modify tables. Schema delivery_...
Measure Daily Late-Order Rates by Delivery Zone
The original interview report identified a late-order SQL exercise but did not preserve its exact schema. The following is a self-contained practice r...
Analyze DoorDash Orders: High-Frequency Customers, Top Spenders, MoM Sales & Bottom-Percentile Reach
orders +-------------+-------------+---------------+---------------------+ | delivery_id | customer_id | restaurant_id | order_place_time | +------...
Calculate Order Request Metrics
You are working with DoorDash order and delivery-request data. Write SQL to answer the questions below. Tables: 1. orders - order_id BIGINT, primary k...
Write SQL for percent and window changes
Use PostgreSQL. Assume today = 2025-09-01. You must use CTEs and multiple window functions. Schema and tiny samples are below. Schema: - exposures(uni...
Measure Late Deliveries and Identify Top Delayed Restaurants
orders +----------+---------+--------------+---------------------+-------------------------+-----------------------+ | order_id | user_id | restaurant...
Model schema and query new-market readiness
Assume today is 2025-09-01. You are given (or can propose) a minimal schema to assess new-market readiness and early performance. Use the schema below...
Write SQL to backtest refund policy
Using the schema and samples below, write a single SQL query (CTEs allowed) that does all of the following for the last 30 days relative to today = 20...
Write complex SQL on DoorDash data
You are given the following BigQuery-style schema and tiny samples (assume timestamps are UTC; assume promotions.discount_amount is the applied discou...
Solve multi-part SQL with sliding windows
Assume 'today' is 2025-09-01. You are given the following tables. users(user_id INT PRIMARY KEY, signup_date DATE) orders(order_id INT PRIMARY KEY, us...
Write SQL for cuisine median delivery times
Use SQL to answer the following. Assume ANSI SQL with window functions and percentile functions available. Treat “today” as 2025-09-01 (inclusive). Co...
Generate Weekly Revenue and Engagement Summary with Pandas
events | user_id | event_time | event_type | platform | revenue | |---------|---------------------|------------|----------|---------| | 101 ...
Find orders from bottom-quartile revenue restaurants
SQL Question You want to identify orders coming from restaurants whose total revenue is in the bottom 25th percentile. Assume the following tables: re...
Compute rolling cold-delivery rates with windows
Assume a food-delivery platform with the following schema. Use PostgreSQL. A delivery is considered "cold" if food_temp_c < 40 at dropoff OR there is ...
Write SQL for cold-complaint diagnostics with LAG/QUALIFY
Using BigQuery/Snowflake-style SQL (CTEs required; use LAG and QUALIFY), answer the tasks below. Assume 'today' is 2025-09-01. Schema and small sample...
Analyze Order Spending Patterns Across Cities Using SQL
Orders order_id | user_id | order_date | city | order_value 1 | 101 | 2023-01-03 | LA | 23.50 2 | 102 | 2023-01-04 | NY | 45.00 3 | 101 | 2023-01-10 |...
Calculate Late Delivery Percentage and Top Customers
Orders +-----------+-------------+------------------------+------------------------+ | order_id | customer_id | expected_delivery_date | actual_deliv...