Projects & systems

At TCS, I own the data platform behind a Fortune-500 bank's batch operations. It pulls job status out of the bank's mainframe, stores it, and powers the dashboards and alerts the client's operations team lives on. I designed the query engine, the failure tracking, and the system that predicts an SLA miss before it happens. The result: our most important batch job went from ~15 minutes to under 1 minute, and the platform now flags problems before the client ever sees them. Under the hood: Python, Oracle, and MongoDB, with a config-driven SQL engine and 85% test coverage across the codebase.

• Cut the core batch job from ~15 minutes to under 1 (93% faster), with results verified unchanged
• Predicts SLA misses before they occur, using job-calendar data
• Catches silent data failures early, instead of after the client does
• 85% test coverage, clean static analysis, and structured logging (70% less log volume)