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Design Food Ratings and Driver Payouts

Last updated: Apr 19, 2026

Quick Overview

This question evaluates system design competencies such as API design, data modeling and storage selection for aggregated 5-point ratings, handling user-generated posts and likes, service decomposition, consistency trade-offs, scalability, and designing a monthly batch payout pipeline for driver earnings.

  • medium
  • DoorDash
  • System Design
  • Software Engineer

Design Food Ratings and Driver Payouts

Company: DoorDash

Role: Software Engineer

Category: System Design

Difficulty: medium

Interview Round: Onsite

Design the backend for a food-delivery product with the following requirements: - Each food item has a detail page that shows an aggregated rating on a 5-point scale. - Each food item also has user-generated posts attached to it. - Users can like posts. - At the end of each month, the system must calculate and settle each driver's earnings based on completed deliveries. Discuss the functional requirements, APIs, data model, storage choices, service decomposition, rating aggregation strategy, post-like handling, monthly payout pipeline, consistency requirements, and how the system should scale.

Quick Answer: This question evaluates system design competencies such as API design, data modeling and storage selection for aggregated 5-point ratings, handling user-generated posts and likes, service decomposition, consistency trade-offs, scalability, and designing a monthly batch payout pipeline for driver earnings.

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DoorDash logo
DoorDash
Feb 3, 2026, 12:00 AM
Software Engineer
Onsite
System Design
27
0

Design the backend for a food-delivery product with the following requirements:

  • Each food item has a detail page that shows an aggregated rating on a 5-point scale.
  • Each food item also has user-generated posts attached to it.
  • Users can like posts.
  • At the end of each month, the system must calculate and settle each driver's earnings based on completed deliveries.

Discuss the functional requirements, APIs, data model, storage choices, service decomposition, rating aggregation strategy, post-like handling, monthly payout pipeline, consistency requirements, and how the system should scale.

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