ABHISHEK JENA

Diagnosed recurring N+1 query problem on high-traffic endpoints where a single request silently triggered hundreds of extra lazy-loaded queries; rewrote fetch strategy to eager-load related entities in one query, cutting API response time by 62% (from 850ms to 320ms).

Identified that a rapidly growing user base caused excessive database queries per request on list/dashboard endpoints; batched queries and introduced caching for repeat reads, reducing average DB round-trips per request by 70% and cutting page load time by 45%.

Traced a memory leak in a long-running background service back to unclosed resources and an unbounded in-memory cache; fixed resource cleanup and added cache eviction limits, bringing heap usage down by 38% and eliminating recurring service restarts.

Identified missing pagination on an endpoint returning full table scans to the frontend, causing timeouts under load; implemented cursor-based pagination and response size limits, reducing endpoint latency by 55% and preventing OOM errors during peak traffic.

Analyzed query execution plans (EXPLAIN) behind slow reporting queries flagged by users; designed and added composite indexes matching actual query patterns, slashing query runtime from 4.2s down to 180ms.

Uncovered a stale cache bug causing users to see outdated data after updates; engineered cache invalidation logic to trigger atomically on writes, eliminating the class of reported data-inconsistency bugs.

Debugged intermittent production incidents by tracing logs, distributed stack traces, and slow-query plans back to root causes, cutting average incident resolution time (MTTR) by 40%.

Tightened access control after discovering gaps in role checks on several internal endpoints, hardening authentication and closing unauthorized-access risk down to zero reported incidents.