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

Senior

Jul 2026

Summary

System design round at Oscar for a software engineering role. The whole thing was one big open-ended question about building a telehealth queuing system, which sounds manageable until you realize how many moving pieces they actually want you to cover.

Questions Asked (1)

Design a web-based queuing system for a health insurance company's 24/7 telehealth platform, where members request virtual consultations and physicians (each licensed only in certain states) are matched and assigned to those requests in a compliant way. Cover requirements, APIs, data model, queuing and matching logic, concurrency, scalability, reliability, and operational concerns.

System DesignData ModelingAPI & Integrations

Author's notes

This one sprawled fast.

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AI HintsAI Generated

Suggested Approach

Start by clarifying functional and non-functional requirements, especially compliance constraints like state licensure and 24/7 availability. Then design the system end-to-end: APIs, data model, queuing/matching logic, concurrency handling, scalability, reliability, and operational monitoring. Emphasize how you ensure compliant physician assignment and handle real-time matching at scale.

Pro tip: Explicitly call out that licensure compliance is a hard constraint that must be enforced at the matching layer, not just filtered later. Also, discuss how you'd handle state-specific licensing changes dynamically without downtime.

1. Clarify Requirements and Constraints

Ask questions to understand scale (e.g., requests per second, number of physicians), latency expectations, compliance rules (state licensure, HIPAA), and 24/7 operational needs. Define functional requirements like request submission, matching, assignment, and status tracking.

2. Design APIs and Data Model

Define RESTful or gRPC APIs for members (submit request, check status) and physicians (update availability, accept assignment). Design a data model with entities: Member, Physician (with licensed states), ConsultationRequest, Assignment, and AuditLog, ensuring indexes for efficient querying.

3. Design Queuing and Matching Logic

Use a priority queue (e.g., Redis sorted sets or Kafka) to hold pending requests, prioritized by urgency and wait time. Implement a matching service that filters physicians by state licensure, availability, and specialty, then assigns using a fair algorithm (e.g., round-robin or least-recently-assigned).

4. Address Concurrency, Scalability, and Reliability

Use distributed locks or optimistic concurrency to prevent double-assignment. Scale horizontally with stateless services and sharded queues. Ensure reliability via replication, failover, and idempotent operations. Consider eventual consistency for availability updates.

5. Cover Operational Concerns

Discuss monitoring (queue depth, assignment latency, licensure compliance), alerting, logging, and auditing. Plan for dynamic updates to physician licensure and handling peak loads with auto-scaling. Include disaster recovery and data retention policies.

Key Points to Mention

State licensure compliance as a hard filter in matching, with real-time updates to physician credentials.
Use of a distributed queue (e.g., Kafka, Redis) for decoupling and handling bursts.
Concurrency control to avoid double-booking physicians (e.g., distributed locks, database transactions).
Scalability via horizontal scaling, sharding by state or region, and caching physician availability.
Reliability through redundancy, idempotent APIs, and graceful degradation (e.g., fallback to manual assignment).
Operational monitoring for queue health, assignment latency, and compliance audits.

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