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

SeniorPrefer not to say
Apr 2026

Summary

Lyft system design interview, one question about building an identity platform. Pretty open-ended and I wasn't totally sure what scope they wanted.

Questions Asked (1)

Q1

Design an identity platform for Lyft.

System DesignTechnical Trade-offsProduct Strategy
Author's notes

Spent the first few minutes just trying to nail down what 'identity' even meant in this context.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the scope of the identity platform for Lyft, focusing on core use cases like rider/driver authentication, authorization, and profile management. Then, outline a high-level architecture that balances security, scalability, and user experience, and dive into key components like OAuth 2.0/OpenID Connect, token management, and multi-factor authentication. Finally, discuss trade-offs around build vs. buy, data consistency, and compliance (e.g., GDPR, CCPA).

Pro tip: Emphasize Lyft's unique challenges: real-time ride matching requires low-latency auth, and driver identity verification must be robust to prevent fraud. Mention how you'd leverage existing Lyft services (e.g., payment, fraud detection) to avoid reinventing the wheel.

1. Clarify Requirements and Scope

Ask clarifying questions to understand the scale (millions of users), key features (SSO, MFA, social login), and constraints (latency, compliance). Define what 'identity' means for Lyft: riders, drivers, and internal services.

2. High-Level Architecture

Sketch a microservices-based architecture with an identity provider (IdP) that handles authentication, authorization, and user management. Include components like API gateway, token service, user store, and integration with external IdPs (Google, Apple).

3. Deep Dive into Key Components

Detail the authentication flow (OAuth 2.0/OpenID Connect), token management (JWT, refresh tokens), and security measures (rate limiting, anomaly detection). Discuss data storage choices (SQL vs. NoSQL) for user profiles and session data.

4. Address Trade-offs and Challenges

Discuss trade-offs: build vs. buy (e.g., using Auth0 vs. in-house), consistency vs. availability (CAP theorem), and security vs. user experience. Highlight Lyft-specific challenges like real-time driver verification and fraud prevention.

5. Scalability, Reliability, and Compliance

Explain how to scale horizontally, handle failures (circuit breakers, retries), and ensure compliance with regulations (GDPR, CCPA). Mention monitoring, logging, and auditing for security.

Key Points to Mention

  • OAuth 2.0 and OpenID Connect for standardized authentication and authorization.
  • Multi-factor authentication (MFA) and biometrics for driver identity verification.
  • Token management: JWT for stateless auth, refresh tokens for long-lived sessions, and token revocation.
  • Data partitioning and sharding for user stores to handle Lyft's scale.
  • Integration with existing Lyft services (payment, fraud detection) and external identity providers.
  • Compliance and privacy: GDPR, CCPA, and secure handling of PII.

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