While working on a data‑intensive analytics feature, the team needed to store both transactional and unstructured data.

Determine the appropriate database technology for each data type.

Described that relational databases provide ACID compliance, structured schemas, and complex joins ideal for transactional data, whereas NoSQL databases offer flexible schemas, horizontal scaling, and high write throughput suitable for unstructured or semi‑structured data like logs or user activity. Cited examples such as PostgreSQL for order processing and MongoDB for event tracking.

Selected PostgreSQL for core transactions and MongoDB for analytics, resulting in a 30% reduction in query latency for reporting and maintained data integrity for orders.

The e‑commerce site expected spikes of up to 10,000 requests per second during sales events.

Create an API architecture that can handle high traffic while ensuring low latency and reliability.

Proposed a layered architecture: load balancer (e.g., NGINX) routing to stateless API servers built with Spring Boot, using JWT for authentication. Employed API gateway for routing and rate limiting, caching frequently accessed data with Redis, and asynchronous processing via message queues (Kafka) for order placement. Data stored in a sharded PostgreSQL cluster with read replicas. Implemented CI/CD pipelines with Docker and Kubernetes for auto‑scaling.

The design supported a 5× traffic increase with average response time under 200 ms and zero downtime during peak sales.

During a Black Friday sale, the checkout service started returning 500 errors, causing revenue loss.

Identify and resolve the root cause within 30 minutes to restore checkout functionality.

Used centralized logging (ELK) to trace the error to a memory leak in a recent code deployment. Rolled back the offending release, reproduced the issue locally with a stress test, added heap size monitoring, and implemented a fix to release resources properly. Communicated status updates to stakeholders throughout the process.

Service restored within 25 minutes, preventing an estimated $200k loss, and the fix was deployed with additional monitoring to avoid recurrence.

Our product team needed a new user profile endpoint to display data on the web app.

Design and deliver a RESTful API that meets front‑end requirements for data shape and performance.

Held a joint planning session with front‑end engineers to gather requirements, defined the JSON schema, and agreed on pagination and error handling conventions. Implemented the endpoint using Spring Boot, added OpenAPI documentation, and set up a mock server for front‑end testing. Conducted a code review and iterated based on feedback.

The API was integrated smoothly, reducing front‑end development time by two weeks and receiving positive feedback for clear documentation and performance.