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

SeniorPrefer not to say
Jun 2026

Summary

Stripe system design round for a software engineer role. One meaty scheduling problem that looked straightforward until the edge cases started piling up.

Questions Asked (1)

Q1

Given a list of users each with a subscription start time and a notification cadence (daily, weekly, or custom interval), design a system that produces a schedule of email notifications to send over a given time window. Return a chronologically ordered list of notification events and discuss how you'd scale this to millions of users.

System DesignAlgorithms & Data StructuresTechnical Trade-offs
Author's notes

I spent the first few minutes just generating timestamps and almost forgot to ask anything.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then outline a two-part solution: an algorithm to generate the notification schedule for a single user and a scalable architecture to handle millions of users. Discuss trade-offs between batch processing and real-time generation, and how to ensure chronological ordering and fault tolerance.

Pro tip: Emphasize idempotency and exactly-once delivery semantics, as duplicate or missed notifications can erode user trust and are critical in a payments company like Stripe.

1. Clarify Requirements and Constraints

Ask about the expected scale, time window size, cadence types, time zones, and whether notifications can be batched or must be sent individually. Confirm if the schedule is precomputed or generated on-demand.

2. Design Single-User Schedule Generation

For each user, compute the next notification times based on their start time and cadence within the given window. Use efficient date arithmetic and handle edge cases like custom intervals and time zones.

3. Aggregate and Sort Events

Collect all notification events from all users and sort them chronologically. Consider using a priority queue or merge sort if processing in batches to avoid loading everything into memory.

4. Scale to Millions of Users

Propose a distributed architecture: shard users by user ID or time zone, process shards in parallel, and use a message queue to decouple schedule generation from sending. Discuss storage and indexing for efficient retrieval.

5. Address Reliability and Trade-offs

Discuss idempotency, retries, dead-letter queues, and monitoring. Compare batch vs. real-time processing, and explain how to handle failures and ensure exactly-once delivery.

Key Points to Mention

  • Time zone handling and daylight saving time adjustments
  • Efficient algorithms for generating intervals (e.g., using modular arithmetic)
  • Sharding strategies (by user ID, time zone, or cadence) for horizontal scaling
  • Use of distributed queues (e.g., Kafka, SQS) and workers for parallel processing
  • Idempotency keys and deduplication to prevent duplicate notifications
  • Monitoring and alerting for missed or delayed notifications

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