My Data Journey (So Far)

My data engineering journey began at Sundaram Finance, where I engineered financial data pipelines processing over 100,000 transactions monthly. I built ETL frameworks using Apache Spark that transformed how we handled millions of financial records, reducing processing times from hours to minutes. Working with insurance and loan data taught me the critical importance of data integrity alongside performance optimization. Moving to The Hartford expanded my expertise into claims analytics, where I developed solutions processing 250,000 claims monthly. I designed real-time validation systems that cut claim review times from 24 hours to under 6 hours, directly impacting business operations. Implementing these enterprise-scale data solutions required mastering cloud architectures and understanding complex data governance requirements. Throughout my career, I've gravitated toward solving challenging data problems. My work with Apache Kafka established resilient streaming architectures that handled time-sensitive financial transactions with minimal latency. When orchestrating complex workflows, I turned to Apache Airflow to manage dependencies between hundreds of daily data tasks while maintaining clear visibility into our processes. One of my most significant technical evolutions came with adopting Snowflake's cloud data platform. Its separation of storage and compute resources revolutionized our analytical capabilities, enabling simultaneous support for various departmental needs while maintaining a single source of truth for all financial data assets. My projects have consistently linked technical implementations to business outcomes—whether developing fraud detection systems that identify suspicious activities in high-value transactions or building interactive dashboards that transform how financial teams make decisions. The satisfaction comes from seeing how optimized data pipelines and models directly improve operations, from faster loan approvals to more accurate risk assessments.