-->Developed a robust AWS-based serverless ETL framework to support client data warehousing and reporting needs, delivering a scalable, reliable, and cost-efficient solution--> Designed and implemented a Python script to filter Protected Health Information (PHI) data directly from source database within the ETL workflow, enhancing data compliance--> Developed functionality to sync data by filtering out deleted patient at source, ensuring accurate reporting using MySQL and Phyton scripting for Data Modeling--> Enhanced AWS Step Function workflows by integrating automated success notification emails after completing each layer of processing--> Automated daily Data Processing tasks, including extract creation, data quality checks (QCs), and logging QC results with timestamps--> Introduced error-handling mechanisms to halt data refreshes upon QC failures, triggering failure notifications with precise error details for faster resolution using AWS Lambda and Tableau Server API