DoorDash·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026Remote

Summary

DoorDash system design round focused on a service aggregation problem. Pretty OOD-heavy, more code than I expected for what felt like a design question.

Questions Asked (1)

You have three separate services that each return a partial view of the same entity. Build a new service that calls all three, merges the results into one response, and handles cases where one of the upstream services fails or times out.

System DesignAPI & IntegrationsTechnical Trade-offs

Author's notes

I started with the happy path which was the right call, but I spent too long on the data models before touching the aggregator logic.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then design the aggregation service with a focus on parallel calls, partial failure handling, and response consistency. Discuss trade-offs between consistency, latency, and complexity, and propose a concrete implementation with timeouts, retries, and fallbacks.

Pro tip: Emphasize idempotency and observability: ensure the aggregation service can be safely retried and that failures are logged with enough context to debug upstream issues. Also, consider using a circuit breaker to prevent cascading failures.

1. Clarify Requirements and Constraints

Ask about expected latency, consistency requirements (e.g., can we return partial data?), and failure semantics (e.g., should we fail fast or degrade gracefully?).

2. Design the Aggregation Service

Outline the service architecture: it receives a request, fans out to the three upstream services in parallel, and merges responses. Use async I/O or a thread pool to avoid blocking.

3. Handle Failures and Timeouts

Define per-call timeouts, retry policies (with backoff), and fallback strategies (e.g., return partial data with warnings, or use cached data). Consider circuit breakers to avoid overwhelming failing services.

4. Merge and Validate Responses

Describe how to merge partial views: handle conflicts (e.g., last-write-wins, or field-level precedence), validate data integrity, and ensure the merged response conforms to the expected schema.

5. Discuss Trade-offs and Operational Concerns

Talk about consistency vs. availability, latency vs. completeness, and monitoring/alerting. Mention idempotency, caching, and how to test failure scenarios.

Key Points to Mention

Parallel calls to upstream services to minimize latency
Timeout and retry strategies with exponential backoff and jitter
Fallback mechanisms: partial responses, cached data, or default values
Circuit breaker pattern to prevent cascading failures
Idempotency of the aggregation service to allow safe retries
Observability: logging, metrics, and tracing for debugging and monitoring

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