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Amazon·Software Engineer·Onsite - System Design / Architecture·Intermediate

IntermediatePrefer not to say
Apr 2026

Summary

Amazon system design round for a software engineer role. The question was about building a task management system, which sounds straightforward until you realize they want you to cover basically everything: data modeling, APIs, storage choices, thread safety, ID generation, performance, and testing. A lot of ground for one question.

Questions Asked (1)

Q1

Design a task management system that supports adding tasks with unique IDs, marking tasks complete, retrieving incomplete and completed tasks, and fetching a task by ID. Cover the data model, core APIs, storage approach, thread safety, ID generation, performance, and testing strategy.

System DesignTechnical Trade-offsData Modeling
Author's notes

I started with the data model, which felt safe, then moved into the API surface.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then propose a clean data model and API contract. Walk through storage, concurrency, and ID generation choices with trade-offs, and finish with performance and testing considerations. Emphasize simplicity and extensibility, aligning with Amazon's leadership principles.

Pro tip: Proactively discuss trade-offs (e.g., in-memory vs. persistent storage, locking vs. lock-free) and tie decisions to business needs like scalability and reliability. This shows you think like an owner, not just a coder.

1. Clarify Requirements and Scope

Ask about expected scale (tasks per user, concurrent users), persistence needs, and whether this is a single-user or multi-user system. Confirm functional and non-functional requirements.

2. Define Data Model and APIs

Specify a Task entity with fields like id, title, description, status, createdAt, completedAt. Outline core API methods: addTask, completeTask, getIncompleteTasks, getCompletedTasks, getTaskById.

3. Choose Storage and Concurrency Strategy

Decide between in-memory (e.g., HashMap) and persistent storage (e.g., database). Discuss thread safety using synchronized collections, locks, or concurrent data structures, and justify based on scale and consistency needs.

4. Address ID Generation and Performance

Propose ID generation (UUID, auto-increment, Snowflake) with trade-offs. Analyze time complexity of operations and suggest indexing or caching for performance.

5. Outline Testing Strategy

Describe unit tests for each API, concurrency tests to ensure thread safety, and integration tests if using a database. Mention edge cases like duplicate IDs and invalid task IDs.

Key Points to Mention

  • Data model: Task entity with unique ID, status, timestamps, and other relevant fields.
  • API design: CRUD-like operations with clear contracts and error handling.
  • Storage: In-memory vs. persistent, with trade-offs on durability, scalability, and complexity.
  • Thread safety: Use of synchronized blocks, ConcurrentHashMap, or database transactions.
  • ID generation: UUID vs. auto-increment vs. distributed ID generators, considering uniqueness and performance.
  • Performance: O(1) operations with hash-based structures, indexing for queries, and caching strategies.
  • Testing: Unit tests, concurrency tests, and integration tests with edge cases.

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