Developed an enterprise-grade AI and cloud analytics platform for the insurance industry. The project automates insurance data ingestion, ETL processing, document intelligence, and analytics using AWS Glue, Amazon S3, Python, and Generative AI. It processes data from multiple insurance providers, converts it into optimized Parquet datasets, and supports business intelligence dashboards for operational monitoring. The platform significantly reduced manual effort, improved data quality, and accelerated reporting for business teams.
If I were to do this project again, I would adopt a lakehouse architecture from the beginning, implement Infrastructure as Code (Terraform), automate CI/CD pipelines, and use Amazon Redshift Serverless instead of provisioning clusters. I would also incorporate comprehensive data quality checks, monitoring, and AI-powered anomaly detection earlier in the development lifecycle to improve scalability, reliability, and operational efficiency.
