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

SeniorPrefer not to say
May 2026

Summary

Did a system design round at Stripe for a software engineer role. The prompt was a superhero dispatch system, which sounds ridiculous but was actually a decent design exercise. Feedback afterward was that I didn't justify my tech choices well enough.

Questions Asked (1)

Q1

Design a superhero dispatch system where regular people submit rescue requests and available superheroes can pick them up.

System DesignTechnical Trade-offsAdaptability & Ambiguity
Author's notes

Completely unprepared for this theme.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then design a high-level architecture that decouples request submission from hero dispatch using a queue and matching service. Focus on core components like request intake, hero availability, matching algorithm, and notification, while discussing trade-offs around consistency, latency, and scalability.

Pro tip: Frame the problem as a two-sided marketplace with real-time constraints, and explicitly call out how you'd handle race conditions when multiple heroes try to accept the same request—this shows you think about concurrency and reliability, which is crucial for Stripe's payment systems.

1. Clarify Requirements and Scale

Ask questions to understand functional and non-functional requirements: expected request volume, number of heroes, geographic distribution, latency needs, and consistency guarantees. Define the MVP scope and what can be relaxed.

2. High-Level Architecture

Sketch the main components: API gateway for request submission, a queue or event stream for incoming requests, a matching service that pairs requests with available heroes, a hero availability service, and a notification service. Explain data flow and interactions.

3. Deep Dive into Matching and Concurrency

Detail the matching algorithm (e.g., based on proximity, hero skills, and current load) and how to handle concurrent acceptances using optimistic locking or a first-write-wins approach. Discuss how to ensure exactly-once assignment.

4. Data Model and Storage

Propose schemas for requests, heroes, and assignments. Choose appropriate databases (e.g., relational for transactions, geospatial index for location) and discuss consistency vs. availability trade-offs.

5. Scalability, Reliability, and Trade-offs

Address scaling (sharding, partitioning), fault tolerance (retries, dead-letter queues), and monitoring. Discuss trade-offs like latency vs. consistency, and how to evolve the system over time.

Key Points to Mention

  • Use of a message queue (e.g., Kafka, RabbitMQ) to decouple request submission from dispatch and handle spikes.
  • Geospatial indexing (e.g., Redis GEO, PostGIS) for efficient proximity-based matching.
  • Concurrency control (e.g., optimistic locking, distributed locks) to prevent double assignment.
  • Idempotency and exactly-once processing to avoid duplicate rescues.
  • Scalability considerations: horizontal scaling of services, partitioning by region.
  • Trade-offs between consistency and availability (CAP theorem) in a real-time dispatch system.

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