Engineered a monitoring framework using Python, React for daily commodity levels, collaboratively ideating, designing, and developing with cross-functional teams. The framework drastically reduced risk miscalculations by 95%, efficiently flagging incorrect and missing levels.• Improved snapper process stability by implementing subscriptions from diverse sources, objects, and retries for both end-of-day and intraday data snapshots, achieving 90% reduction in noise for the strategy.• Enhanced the rebalancing framework through the integration of a stream listener, effectively responding to new requests and orchestrating actions through chain of responsibilities. This optimization led to 20% acceleration in processing speed and an overall improvement in application performance.

• Achieved a 94% reduction in work time by automating the re-submission process through web service.• Implement best security practices by removing local file inclusion and high risk vulnerability• Daily BAU support to resolve queries coming from application users and facilitating them to reach to right solution to prepare and deliver their reports on time.