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

SeniorPrefer not to say

May 2026

Summary

A system design round at Snowflake that felt nothing like a typical system design interview. The question was apparently pulled from a real incident, which made it interesting but also left me completely unsure what they were actually testing for.

Questions Asked (1)

You control a client that periodically fetches a token from a third-party service and then uses that token to access a backend. How would you design the client to minimize downtime if either the token service or the backend becomes unavailable?

System DesignTechnical Trade-offsAdaptability & Ambiguity

Author's notes

The twist that tripped me up: both the token service and the backend are black boxes.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements such as acceptable downtime, token TTL, and failure modes. Then propose a resilient design with caching, retries, and fallbacks, and discuss trade-offs between consistency and availability.

Pro tip: Emphasize proactive measures like monitoring token expiry and using circuit breakers to prevent cascading failures. Show you consider operational aspects, not just code.

1. Clarify Requirements

Ask about acceptable downtime, token lifetime, request volume, and whether stale tokens can be used. This shows you avoid assumptions.

2. Design for Token Service Failures

Cache tokens with a safety margin before expiry, implement retries with exponential backoff, and consider a fallback token source or degraded mode.

3. Design for Backend Failures

Use retries, circuit breakers, and possibly a local cache of recent responses. Discuss idempotency and queuing requests if appropriate.

4. Handle Token Expiry During Backend Outage

If the backend is down, you may not need a token immediately. But if the token expires while backend is down, ensure you can refresh it when backend recovers.

5. Discuss Trade-offs and Monitoring

Balance consistency vs availability, and mention monitoring, alerting, and logging to detect and respond to failures quickly.

Key Points to Mention

Token caching with expiry buffer
Retry with exponential backoff and jitter
Circuit breaker pattern to avoid hammering failing services
Fallback strategies: stale token, degraded mode, or queuing
Idempotency of backend requests
Monitoring and alerting for token refresh failures and backend errors

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