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DoorDash·Software Engineer·Onsite - System Design / Architecture·Intermediate

Intermediate
Jun 2026

Summary

DoorDash data engineer interview with a system design focus. Just one question but it had a lot of surface area and I don't think I covered it as well as I could have.

Questions Asked (1)

Q1

Design a database schema for a fitness app.

Data ModelingSystem DesignTechnical Trade-offs
Author's notes

I started with users and workouts and thought I was on the right track, but then realized I had no clean way to model exercises within a workout without creating a mess of junction tables.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the scope and key features of the fitness app (e.g., user profiles, workouts, tracking, social features). Then design a normalized relational schema that balances flexibility, scalability, and performance, explicitly discussing trade-offs and indexing strategies.

Pro tip: Demonstrate awareness of read-heavy vs. write-heavy patterns and propose denormalization or caching for frequently accessed data like activity feeds, showing you think beyond just tables.

1. Clarify Requirements

Ask about core features, expected scale, and data access patterns to define the schema's scope and priorities.

2. Identify Entities and Relationships

List main entities (User, Workout, Exercise, ActivityLog, etc.) and define their relationships (one-to-many, many-to-many).

3. Design Tables and Keys

Create normalized tables with primary and foreign keys, ensuring data integrity and avoiding redundancy.

4. Optimize for Performance

Add indexes on frequent query columns, consider partitioning for large tables, and discuss denormalization for read-heavy features.

5. Discuss Trade-offs and Scalability

Explain choices like SQL vs. NoSQL, normalization vs. denormalization, and how the schema supports future growth.

Key Points to Mention

  • Normalization to reduce redundancy and maintain data integrity
  • Indexing strategy for common queries (e.g., user activity by date)
  • Handling many-to-many relationships (e.g., users and workouts) with junction tables
  • Scalability considerations: sharding, partitioning, or using a NoSQL store for specific use cases
  • Data types and constraints (e.g., timestamps, enums for workout types)
  • Trade-offs between read performance and write performance, and when to denormalize

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