Work Experience

Hartford Financial Service Group

I built scalable ETL pipelines using Azure Data Factory and Airflow, optimizing claims processing for 250,000+ claims per month. I implemented real-time claims validation with Azure Event Hubs and Kafka, reducing review time from 24 hours to under 6. I optimized actuarial data models in Azure Synapse, cutting data retrieval time by 75%. I also automated compliance workflows, securing 500GB+ of sensitive data, and developed Power BI dashboards, improving decision-making speed. My CI/CD automation in Azure DevOps reduced manual intervention and improved ETL reliability.

Used Skills: Python(Programming Language), R, SQL, Microsoft Azure, Apache Airflow, Apache Kafka, PowerBI, Docker, Snowflake

Sundaram Finance

led the development of data engineering solutions focused on health insurance operations, supporting the processing of over 120,000 health-related transactions monthly. I designed and automated end-to-end claim workflows using AWS Glue and Apache Spark, significantly improving processing efficiency and data accuracy. To enhance real-time decision-making, I implemented monitoring pipelines using AWS Kinesis and Lambda that detected anomalies in health claims within seconds, strengthening fraud detection capabilities. As part of the underwriting process, I integrated secure data ingestion mechanisms to request and process medical records from Apollo Health when applicants submitted new insurance requests. These efforts enabled seamless, high-throughput handling of health data while maintaining strict controls around data integrity, privacy, and operational scalability.

Used Skills: Python(Programming Language), R, SQL, AWS, Apache Airflow, PowerBI, Docker, Kafka, Tableau