I jumped straight into the data export flow without thinking about scale first, which I think hurt me.
Start by clarifying the scope and requirements: what data types are included, expected scale, latency, and compliance needs. Then outline a high-level architecture covering data aggregation, asynchronous processing, secure delivery, and user notification, while discussing trade-offs between synchronous and asynchronous approaches. Finally, dive into key components like API design, storage, and security, and tie back to product metrics and user experience.
Pro tip: Emphasize the importance of idempotency and rate limiting to prevent abuse and ensure reliability, and mention how you'd handle partial failures and retries gracefully. Also, highlight the need for a clear user communication strategy (e.g., email with download link, expiration) to set expectations.
Ask questions to understand the scope: data types (posts, messages, photos), volume, frequency of requests, latency expectations, and compliance (GDPR, CCPA).
Propose an asynchronous, queue-based system: user request triggers a job, which aggregates data from various services, packages it, stores it in blob storage, and notifies the user with a secure link.
Detail key components: API endpoint for request, job queue (e.g., Kafka), workers to fetch data from microservices, data format (e.g., JSON/CSV), compression, encryption, and storage (e.g., S3).
Discuss authentication/authorization, encryption at rest and in transit, link expiration, and audit logging. Ensure data privacy and regulatory compliance.
Compare synchronous vs asynchronous, cost vs performance, and discuss success metrics (e.g., time to download, success rate) and failure handling (retries, dead-letter queues).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.