• Engineered a priority-aware data pipeline using Kafka, processing over 600,000 messages daily. This solution accelerated priority data availability, saving up to 5 hours on critical days thus enabling timely daily firmwide revenues reporting.• Designed and implemented an exception-driven workflow using Java, Elastic, Snowflake and AWS based architecture, effectively capturing and resolving discrepancies totaling over $20 million in firmwide P&L.• Set up a scalable ETL pipeline on AWS using Glue, S3, Lambda, SQS, and Kafka to migrate 100 million daily on-prem P&L records to Snowflake. This migration co-located data for faster, more scalable queries and significantly reduced infrastructure costs.• Led the migration of a P&L sign-off workflow, managing daily P&L for firmwide traders, from a legacy email-based system to strategic internal platforms leveraging S3 and Kafka. This transition reduced P&L report render time by over 50% and significantly improved the accuracy of P&L reporting.• Developed a robust framework for active monitoring and alerting of critical services, utilizing custom-defined SLOs and tools like Prometheus. This initiative led to a significant reduction in severe incidents and a decrease in mean time to resolution (MTTR), while also enabling service owners to proactively identify and address potential gaps.• Designed and decoupled critical P&L transferring data pipes using Kafka for the legacy and the strategic data sources enhancing availability and significantly reducing operational costs.