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

SeniorPrefer not to say

Apr 2026

Summary

DoorDash system design round, one big open-ended question about building an aggregator API on top of three downstream services. Felt like a reasonable problem but the scope kept expanding once I thought I had a handle on it.

Questions Asked (1)

Design and implement an aggregated API endpoint that calls three downstream services, pulls the relevant fields from each, and merges everything into a single unified response. Define the response schema, the downstream interfaces, and the orchestration logic including parallel calls and error handling. Then discuss what you'd add to make it production-ready.

System DesignAPI & IntegrationsTechnical Trade-offs

Author's notes

I started with the happy path, which in hindsight was the wrong move.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and defining the unified response schema, then design the downstream interfaces and orchestration logic with parallel calls and robust error handling. Finally, discuss production-readiness aspects like caching, resilience, observability, and security, emphasizing trade-offs and scalability.

Pro tip: Emphasize idempotency and graceful degradation: design the endpoint to return partial results with appropriate status codes when some downstream services fail, and ensure it can be safely retried without side effects.

1. Clarify Requirements and Define Schema

Ask clarifying questions about expected latency, consistency, and failure modes. Define the unified response schema, including fields from each service and metadata like partial failure indicators.

2. Design Downstream Interfaces

Specify the API contracts for each downstream service, including request/response formats, timeouts, and error codes. Consider using a common client library or interface abstraction.

3. Implement Orchestration Logic

Use parallel calls (e.g., via CompletableFuture, asyncio, or goroutines) to minimize latency. Implement error handling with timeouts, retries, circuit breakers, and fallbacks, aggregating results and errors.

4. Make It Production-Ready

Add caching, rate limiting, observability (logging, metrics, tracing), security (auth, input validation), and deployment considerations (scaling, config management). Discuss trade-offs like consistency vs. availability.

Key Points to Mention

Parallel execution to reduce latency and avoid sequential bottlenecks.
Error handling strategies: timeouts, retries with exponential backoff, circuit breakers, and fallback responses.
Partial failure handling: return partial data with a 207 Multi-Status or custom error structure, and document behavior.
Caching strategies: response caching, cache invalidation, and TTL considerations for aggregated data.
Observability: structured logging, metrics (latency, error rates), and distributed tracing for debugging.
Security and idempotency: authentication, authorization, input validation, and ensuring GET requests are idempotent.

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