Amazon Data Manipulation (SQL/Python) Interview Questions
Amazon Data Manipulation (SQL/Python) interview questions at Amazon focus on practical problem solving with real-world data: efficient joins and window functions in SQL, group and aggregate logic, NULL-handling and performance tradeoffs, and equivalent Pandas/NumPy patterns for in-memory work. What’s distinctive is the mix of product-sense and scale-awareness — interviewers evaluate both correctness and how your solution would behave on large tables or within a production pipeline. Expect timed live-coding or take-home tasks where clarity of assumptions, test cases, and incremental refinement matter as much as final syntax. For interview preparation, prioritize translating tasks between SQL and Pandas, practicing common subproblems (deduplication, rolling/window calculations, CTE refactors, and merge strategies), and explaining complexity and scalability tradeoffs. Be ready to justify index or partition choices, to optimize a slow query, and to narrate decisions with Amazon’s customer-and-ownership lens. Practicing with real datasets and mock interviews that simulate pressure will improve fluency and the concise communication Amazon looks for.
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Calculate Defect Rate and Identify Top Lanes for Carriers
shipment +-------------+----------+-----------+---------+---------+-------------+-----------+ | shipment_id | order_id | ship_date | carrier | origin ...
Understand SQL: DELETE vs TRUNCATE, VIEW vs TABLE, CROSS JOIN
employees +----+--------+---------+ | id | name | dept_id | +----+--------+---------+ | 1 | Alice | 10 | | 2 | Bob | 20 | | 3 | Car...
Analyze Top 10 Items' Revenue Contribution by Category
sales +----------+------------+---------+---------+------------+ | order_id | category | item_id | revenue | order_date | +----------+------------+-...
Analyze User Engagement with SQL Queries
events +----------+---------+---------------------+ | event_id | user_id | event_time | +----------+---------+---------------------+ | 1 ...
Compute 3-Day Rolling Revenue Averages with Pandas
sales +------------+-----------+-------+---------+ | date | product_id| units | revenue | +------------+-----------+-------+---------+ | 2023-01...
Analyze Monthly Prime vs Non-Prime Sales and Price Buckets
sales +-----------+------------+----------+-------+ | order_id | order_date | is_prime | price | +-----------+------------+----------+-------+ | 1001...
Create Country-Level Spend Report Using Pandas
users +---------+---------+ | user_id | country | +---------+---------+ | 1 | US | | 2 | CA | | 3 | US | +---------+-...
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 ...
Compute unique visitors by department
You have two tables: products(product_id, department, category, subcategory) where department > category > subcategory form a parent–child hierarchy, ...
Design Incremental Load Process for Large Relational Table
orders_daily_load +------------+-----------+-------------+--------+ | load_date | order_id | customer_id | amount | +------------+-----------+------...
Identify SQL Joins and Correct Query Errors
Winner +----+-------+ | ID | Name | +----+-------+ | 1 | Alice | | 2 | Bob | | 3 | Carol | +----+-------+ Loser +----+-------+ | ID | Name | ...
Identify Top Spenders and Segment Customers Using Python
orders +----------+---------+------------+----------+--------------+-------------------+ | order_id | cust_id | order_date | product | order_amount |...
Explore Subscription Patterns and Status Transitions with SQL/Pandas
subscriptions +-----------------+---------+-------------+ | subscription_id | status | status_date | +-----------------+---------+-------------+ | 10...
Consolidate and Rank Global Salaries in USD
employees_us +---------+----------+--------+---------+ | emp_id | name | salary | country | +---------+----------+--------+---------+ | 1 |...
Identify Date with Highest Total kWh Consumption Using Pandas
meter_readings +----------+------------+---------------+---------+ | meter_id | timestamp | kwh_consumed | city | +----------+------------+------...
Calculate Regional Revenue and Identify Top Customers
Customers | customer_id | name | region | |-------------|-------|--------| | 1 | Alice | East | | 2 | Bob | West | | 3 ...
Identify Most Popular First-Watched Movie in Viewing History
MOVIE_VIEWS +------------+--------------------+------------+ | customer_id| title | date | +------------+--------------------+-----...
Analyze Seller Compliance and Customer Purchase Patterns
SELLER_STATUS +-----------+------------+-----------+ | seller_id | date | status | +-----------+------------+-----------+ | 1 |2019-0...
Identify Frequent Actor-Director Collaborations in Film Database
FILMOGRAPHY +-------+---------+------------+ | actor | dir | date | +-------+---------+------------+ | Tom | Nolan | 2020-01-01 | | Tom ...
Identify Customers Purchasing Every Product in Catalog
X_PURCHASES +-------------+---------+ | customer_id | pro_key | +-------------+---------+ | 1 | A | | 1 | B | | 2 ...