Work Experience

Hartford Financial Service Group

I led the modernization of legacy insurance data systems by migrating over 50 Teradata ETL jobs to a scalable, serverless Azure architecture. This transition improved data refresh rates from daily to every six hours and reduced processing times from hours to just minutes. I utilized Azure Synapse and Airflow to streamline data cleansing, orchestration, and automation across key domains like claims, policies, premiums, and customer data—ensuring high reliability and 99% SLA compliance. My work also involved rewriting complex Informatica jobs for fraud detection and risk scoring, building real-time monitoring systems to maintain 99.8% uptime, and automating validation processes that saved dozens of manual hours each month. Additionally, I accelerated the integration of external datasets into the data lake, significantly improving the team’s agility in handling new data sources.

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

Sundaram Finance

I built and managed robust data pipelines that supported large-scale health insurance operations. I migrated 10TB of historical data to Amazon Redshift, cutting down report query times from 45 minutes to under 10 and enhancing access to business insights. Using AWS Glue and PySpark, I created ETL processes that cleaned over 3 million records, eliminated duplicates, and ensured data quality for downstream analytics. I also automated the monthly processing of 250,000+ insurance claims, reducing manual effort by 75% and tripling throughput. To support business users, I developed interactive Tableau dashboards for claims tracking and fraud detection, delivering over 500 reports monthly. My work also involved optimizing cloud storage architecture on AWS S3, which brought a 40% cost reduction while maintaining compliance and availability standards.

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