← Uber Interview Insights

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

SeniorPrefer not to say
Jun 2026

Summary

System design round at Uber for a software engineering role, focused entirely on building an internal driver queue service for pickup zones like airports. Dense question with a lot of moving parts and I'm not sure I covered everything they wanted.

Questions Asked (1)

Q1

Design an internal service that manages a driver queue for each pickup zone (like an airport staging lot). Drivers join the queue when they enter the area and get removed when they leave, go offline, or become ineligible. Internal services should be able to query the queue and see driver ordering. Cover API design, data modeling, event ingestion, queue ordering rules, consistency, scalability, fault tolerance, and observability.

System DesignAPI & IntegrationsData Modeling
Author's notes

This is a big question and I burned probably 10 minutes just trying to scope it before drawing anything.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then design the core data model and API for queue operations. Walk through event ingestion, ordering rules, and consistency guarantees, and finish with scalability, fault tolerance, and observability considerations.

Pro tip: Emphasize idempotency and exactly-once processing for driver events, and discuss how to handle zone-specific ordering rules (e.g., FIFO with priority for certain driver tiers) without sacrificing scalability.

1. Clarify Requirements and Scale

Ask about expected number of zones, drivers per zone, event rate, and consistency requirements (e.g., strong vs eventual). Confirm ordering rules (FIFO, priority, etc.) and query patterns.

2. Design Data Model and API

Propose a data model for queues (e.g., per-zone sorted sets or lists) and define APIs for join, leave, update eligibility, and query (with pagination). Include idempotency keys for mutations.

3. Event Ingestion and Processing

Describe how driver events (enter/leave/offline/ineligible) are ingested (e.g., via Kafka), validated, and processed to update queues. Ensure ordering and exactly-once semantics using event IDs and deduplication.

4. Consistency, Scalability, and Fault Tolerance

Discuss consistency models (e.g., per-zone strong consistency via sharding), scaling strategies (partition by zone, read replicas), and fault tolerance (replication, retries, dead-letter queues).

5. Observability and Monitoring

Outline metrics (queue length, event latency, error rates), logging, tracing, and alerting to ensure system health and debug issues.

Key Points to Mention

  • Idempotent event processing with deduplication to handle retries and at-least-once delivery.
  • Zone-based sharding for scalability and isolation, with per-zone consistency guarantees.
  • Efficient queue ordering using data structures like Redis sorted sets or database indexes.
  • API design with clear contracts, pagination, and versioning for internal consumers.
  • Fault tolerance via replication, graceful degradation, and dead-letter queues for failed events.
  • Observability with metrics, tracing, and alerting on queue depth and event processing latency.

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