← Atlassian Interview Insights
I started with the datastore because that felt most concrete, which in hindsight was a bit backwards.
Start by clarifying requirements and scale, then propose a simple three-tier architecture: a React SPA frontend, a RESTful API backend, and a relational database. Walk through each layer, emphasizing data modeling, API design, and trade-offs like pagination and authentication.
Pro tip: Proactively discuss how you would evolve the design for scale and new features (e.g., images, sharing) to show forward-thinking and avoid over-engineering upfront.
Ask about expected user scale, authentication needs, and whether cards are private or shareable. Confirm that only text is supported and that users can create and view their own cards.
Define a simple schema: a User table (id, username, password_hash) and a Card table (id, user_id, content, created_at). Discuss indexing on user_id for efficient retrieval.
Outline REST endpoints: POST /cards to create a card, GET /cards to list the user's cards (with pagination), and optionally GET /cards/{id}. Include authentication via JWT or session tokens.
Propose a React SPA with two main views: a form to compose a new card and a list view to display existing cards. Use state management (e.g., React Query) for API calls and handle loading/error states.
Address trade-offs like SQL vs NoSQL, pagination strategies, and caching. Mention how to scale (e.g., load balancer, read replicas) and potential future features like sharing or images.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scope and requirements of the system, then describe the User and Card entities with their key attributes, relationships, and constraints. Emphasize how the model supports scalability, data integrity, and common access patterns.
Pro tip: Show awareness of trade-offs: for example, embedding vs. referencing cards in a user document, and how that affects query performance and consistency. Mentioning real-world constraints like pagination or rate limiting demonstrates production maturity.
Ask about expected scale, read/write patterns, and whether the system is relational or NoSQL. State your assumptions to frame the answer.
List core attributes (e.g., userId, email, name, createdAt) and note any sensitive fields. Mention indexes on frequently queried fields like email.
List core attributes (e.g., cardId, userId, cardNumber, expiry, status) and explain how it relates to User. Discuss whether card data is tokenized or stored directly.
Explain that a User can have many Cards (1:N) and how this is modeled (foreign key or embedded array). Mention cascade deletes or soft deletes.
Cover indexes on foreign keys, unique constraints, and how the model handles growth (sharding, partitioning). Mention any denormalization for performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the resource model and relationships (e.g., cards belong to a user), then define RESTful endpoints with appropriate HTTP methods, paths, and status codes. For each endpoint, specify request/response schemas, authentication, and error handling, and briefly discuss pagination for the list endpoint.
Pro tip: Mention idempotency for POST requests using an Idempotency-Key header to prevent duplicate card creation, and discuss how you'd version the API (e.g., /v1/) to support future changes without breaking clients.
Ask clarifying questions about card attributes, user-card relationship, and expected scale. Define the card resource and its fields (e.g., id, title, description, userId, createdAt).
For creation, use POST /v1/cards with a request body containing card details and userId. For listing, use GET /v1/users/{userId}/cards to fetch all cards for a user, supporting pagination via query parameters like limit and offset.
Use GET /v1/cards/{cardId} to fetch a specific card. Ensure the endpoint validates that the card belongs to the authenticated user or that the user has permission to view it.
For each endpoint, detail the JSON schema for requests and responses, and list appropriate HTTP status codes (e.g., 201 for creation, 200 for successful GET, 400 for bad request, 404 for not found, 401/403 for auth issues).
Discuss authentication (e.g., OAuth 2.0 or API keys), rate limiting, error response structure, and versioning strategy. Mention pagination, filtering, and sorting for the list endpoint.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Said session tokens stored server-side with a simple login endpoint, since JWT felt like overkill for an MVP with no third-party clients.
Start by clarifying the MVP's goals and constraints, then propose a simple, secure authentication solution that balances speed of delivery with future scalability. Emphasize trade-offs and justify your choices based on the MVP context.
Pro tip: Show awareness of Atlassian's ecosystem by mentioning integration with Atlassian's existing identity providers (e.g., Atlassian Account) or using a managed service like Auth0 to offload complexity, while noting the trade-off of vendor lock-in.
Ask about the MVP's target users, security needs, and timeline to understand the constraints. This ensures your solution aligns with business goals.
Consider build vs. buy: using a managed service (e.g., Auth0, AWS Cognito) vs. rolling your own (e.g., JWT with OAuth2). Discuss pros and cons of each.
Recommend a specific approach, such as OAuth2 with a third-party provider or a simple email/password with JWT, and explain why it fits the MVP.
Highlight security best practices (e.g., HTTPS, hashing, token expiration) and how the solution can scale or evolve post-MVP.
Recap the key trade-offs (e.g., speed vs. control, cost vs. maintenance) and confirm alignment with MVP goals.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Caching reads, moving to a CDN for static assets, maybe pulling the card creation into an async queue if volume gets high.
Start by clarifying the current architecture and the expected scale (e.g., users, requests per second, data volume) to ground your answer. Then systematically walk through each layer—from client to data storage—identifying bottlenecks and proposing changes that align with Atlassian's product goals and engineering principles. Emphasize trade-offs and incremental evolution rather than a complete rewrite.
Pro tip: Tie your architectural changes to business impact and customer experience—Atlassian values pragmatic solutions that balance scalability with maintainability and cost. Also, mention how you would measure success and validate the changes with load testing and monitoring.
Ask questions to understand the expected scale, performance targets, and constraints (e.g., latency, consistency, budget). State your assumptions explicitly to ensure your answer is relevant.
Analyze the existing MVP architecture to pinpoint likely bottlenecks at each layer: client, API, services, database, and infrastructure. Consider both technical and operational limits.
Suggest specific changes such as horizontal scaling, caching, database sharding, asynchronous processing, and microservices decomposition. Explain how each addresses a bottleneck.
For each proposed change, discuss trade-offs (e.g., complexity, cost, consistency) and why you prefer one approach over another. Show awareness of Atlassian's scale and product needs.
Outline a phased approach to implement changes without disrupting the MVP. Include monitoring, testing, and rollback strategies to ensure reliability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.