Amazon·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

Amazon BI Engineer interview with a SQL-heavy question on customer order behavior. Nothing too crazy but the window function angle required some actual thought.

Questions Asked (1)

Given an orders table with order ID, date, category, and customer ID, write a query that computes: (1) for each day and product category, what fraction of orders placed that day are the customer's first-ever order in that category, and (2) for each day, what fraction of all orders are a customer's first order across any category.

Product Analytics & MetricsData Modeling

Author's notes

Two-part question and I fumbled the second part at first.

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AI HintsAI Generated

Suggested Approach

Use window functions to identify each customer's first order date per category and overall, then join these back to the orders table and aggregate by day and category to compute the fractions. For the first metric, group by day and category and divide the count of orders that are first-in-category by the total orders in that day-category. For the second metric, group by day and divide the count of orders that are first-ever by the total orders that day.

Pro tip: Mention that you would clarify whether 'first order' is determined by the earliest order date or by the lowest order ID in case of ties, and discuss how to handle ties to avoid double-counting.

1. Understand the requirements and clarify ambiguities

Restate the two metrics and ask clarifying questions about tie-breaking (e.g., if a customer has multiple orders on the same day) and whether 'first order' means earliest date or earliest order ID.

2. Identify first orders using window functions

Use ROW_NUMBER() or MIN(order_date) OVER (PARTITION BY customer_id, category) to flag first-in-category orders, and similarly PARTITION BY customer_id for first-ever orders.

3. Compute daily aggregates for each metric

For metric 1, group by order_date and category, count first-in-category orders and total orders, then divide. For metric 2, group by order_date, count first-ever orders and total orders, then divide.

4. Combine results and present final output

Ensure the output includes day, category, fraction_first_in_category, and day-level fraction_first_overall (which may repeat per category or be in a separate result set).

Key Points to Mention

Use of window functions (ROW_NUMBER, MIN OVER) to identify first orders efficiently.
Handling ties: if multiple orders on the same day, use order ID or timestamp to determine the first.
Aggregation logic: counting distinct orders and computing fractions with proper denominators.
Performance considerations: indexing on (customer_id, category, order_date) and avoiding unnecessary joins.
Edge cases: customers with no prior orders, categories with no first orders on a given day, and days with zero orders.
Clear separation of the two metrics and how to present them together or separately.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.