DoorDash·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

DoorDash data engineer round focused entirely on a SQL bucketing problem using their core tables. One question, but it had a lot of moving parts and they clearly wanted to see you think out loud about the design choices, not just produce working syntax.

Questions Asked (1)

Given an ER diagram with merchant, menu, order, and dasher tables, write a SQL query that assigns each merchant to a bucket (low, medium, high) based on a bucketing metric of your choice, such as total order count, total revenue, or order frequency. Justify your choice and return one row per merchant with the merchant ID, the metric value, and the bucket label.

Data ModelingProduct Analytics & MetricsTechnical Trade-offs

Author's notes

I went with total revenue over a trailing 90-day window because it felt more business-relevant than raw order count.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the schema and choosing a bucketing metric that aligns with business goals, such as total order count or revenue. Then write a SQL query that aggregates the metric per merchant and uses window functions or CASE statements to assign buckets. Finally, justify your metric choice and explain how the buckets are defined.

Pro tip: Mention that you would validate the bucket thresholds with stakeholders or use data-driven methods like percentiles to ensure meaningful segmentation. Also, consider handling ties and ensuring the query is efficient for large datasets.

1. Clarify Schema and Metric

Confirm the table relationships and choose a bucketing metric (e.g., total order count) that reflects merchant performance. Justify why this metric is appropriate for the business context.

2. Aggregate Metric per Merchant

Write a subquery or CTE to calculate the chosen metric for each merchant by joining the necessary tables and grouping by merchant ID.

3. Define Bucket Thresholds

Determine the thresholds for low, medium, and high buckets. Use fixed values based on business rules or dynamic values like percentiles (e.g., NTILE) for data-driven segmentation.

4. Assign Buckets and Select Output

Use a CASE statement or window function to assign each merchant to a bucket. Return merchant ID, metric value, and bucket label in the final SELECT.

5. Justify and Optimize

Explain your metric and threshold choices, and mention any optimizations like indexing or filtering to handle large data volumes.

Key Points to Mention

Choice of metric (e.g., total order count) and its business relevance
Use of window functions like NTILE or PERCENT_RANK for dynamic bucketing
Handling of ties and ensuring deterministic bucket assignment
Performance considerations for large datasets (e.g., indexing, avoiding unnecessary joins)
Validation of bucket thresholds with stakeholders or using statistical methods
Clear output format: one row per merchant with merchant ID, metric value, and bucket label

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