J.P. Morgan·Software Engineer·Onsite - System Design / Architecture·Senior

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Jul 2026

Summary

System design round at J.P. Morgan for a software engineering role. The whole session was basically one big URL shortener question that kept branching into follow-ups, so be ready to go deep on every layer.

Questions Asked (1)

Design a URL shortening service like Bitly. Walk through the full system: API design, data model, short-code generation, scalability, caching, reliability, abuse prevention, and the trade-offs you're making.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

This felt manageable at first and then the follow-ups just kept coming.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints (scale, read/write ratio, latency, retention, custom aliases, analytics) to scope the design. Then walk through the full system in a structured way: API, data model, short-code generation, caching, scalability, reliability, abuse prevention, and trade-offs. Emphasize how your choices align with J.P. Morgan's needs for security, compliance, and reliability.

Pro tip: Anchor your design in concrete numbers (e.g., 100M new URLs/day, 10B redirects/day, 99.99% availability) and explicitly discuss trade-offs like consistency vs. latency and cost vs. performance. Mention how you would handle financial-industry-specific concerns such as audit logging, data residency, and rate limiting to prevent abuse.

1. Clarify Requirements and Scope

Ask questions to understand scale, read/write ratio, latency, retention, custom aliases, analytics, and security/compliance needs. Define functional and non-functional requirements.

2. High-Level Design and API

Sketch the core components (API gateway, service, database, cache, analytics) and define the API endpoints (e.g., POST /shorten, GET /{code}). Discuss authentication, rate limiting, and error handling.

3. Data Model and Short-Code Generation

Choose a data store (e.g., key-value store like DynamoDB or Cassandra) and design the schema. Explain short-code generation strategies (hash, base62 of auto-increment ID, pre-generated keys) and how to handle collisions and custom aliases.

4. Scalability, Caching, and Reliability

Describe how to scale reads with caching (Redis/Memcached), database partitioning/sharding, and CDN for redirects. Discuss replication, failover, and monitoring to achieve high availability.

5. Abuse Prevention and Trade-offs

Cover abuse prevention (rate limiting, CAPTCHA, blocklists, spam detection) and summarize key trade-offs (e.g., consistency vs. latency, cost vs. performance, security vs. usability).

Key Points to Mention

Short-code generation: base62 encoding of a distributed ID (e.g., Snowflake) or hash-based with collision resolution; consider pre-generated key pool for low latency.
Data model: key-value store with short_code as key, long_url as value, plus metadata (creation time, user ID, expiration). Use a separate table for analytics.
Caching: cache hot URLs at edge (CDN) and in-memory (Redis) to reduce database load; use LRU eviction and TTL.
Scalability: horizontal scaling of stateless services, database sharding by short_code, read replicas, and asynchronous analytics processing.
Reliability: multi-AZ deployment, replication, health checks, circuit breakers, and graceful degradation (e.g., serve cached redirects if DB is down).
Abuse prevention: rate limiting per IP/user, CAPTCHA for anonymous creation, blocklists for malicious domains, and monitoring for anomalous patterns.

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