Coinbase·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

SQL round for an MLE role at Coinbase, basically one big query problem that looked manageable until I started second-guessing the window function choice.

Questions Asked (1)

Given a products table and an order_items table, compute each product's total revenue for calendar year 2024, then return the top 3 products per category ranked by revenue. Ties should be broken by smaller product_id first. Output should include category, product_id, total_revenue, and rank within category, sorted by category then rank then product_id. Products with zero 2024 sales should be excluded.

Data ModelingAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

The aggregation part was fine, filter on the year, group by product_id, sum quantity times unit_price.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the schema and business rules, then outline a SQL solution using filtering, aggregation, and window functions. Emphasize correctness, performance, and edge cases like ties and zero sales.

Pro tip: Mention that you would validate the query against sample data and consider indexing strategies for large-scale production use, showing awareness of real-world deployment.

1. Clarify requirements and schema

Confirm table structures, date ranges, and tie-breaking rules. Ask about data volume and expected output format.

2. Filter and aggregate revenue

Join products and order_items, filter for 2024 orders, and compute total revenue per product using SUM and GROUP BY.

3. Rank products within categories

Use a window function like ROW_NUMBER() or RANK() partitioned by category, ordered by revenue DESC and product_id ASC to handle ties.

4. Select top 3 per category and format output

Filter for rank <= 3, exclude zero-revenue products, and order by category, rank, and product_id.

5. Discuss performance and edge cases

Talk about indexing, handling large datasets, and potential pitfalls like NULLs or duplicate product entries.

Key Points to Mention

Use of window functions (e.g., ROW_NUMBER, RANK) for ranking within groups
Correct tie-breaking logic: revenue DESC, product_id ASC
Filtering for calendar year 2024 and excluding zero sales
Efficient join and aggregation strategies
Output sorting and formatting requirements
Scalability considerations for large datasets

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