Involved in the management of commercial data for a Life Sciences based client and development of data ingestion and processing framework for different data sources like Azure SQL DB, Global ADLS, Sharepoint, Shared Inbox using their route to market approach. Applied the business rules and tranformations using Databricks and Stored Procedures while orchestrating the entire flow using configuration templates in an optimized manner. Creating KPI's for the master files using an automated mechanism. Maintaning data consistency using data validation rules and handling errors using error logging mechanism and delivering the curated data to the client for reporting and analytics. Shared the knowledge and best practices with the team to promote continuos learning and skill development.

Involved in the migration of data from various data sources like ADLS Gen1, ADLS Gen2, Azure SQL DB, Azure Synapse. The entire process was orchestrated through Azure Data Factory pipelines and Databricks notebooks which handled the data movement and conversion to other formats with and error logging mechanism to handle the exceptions.Creating a data ingestion and processing project from data sources like IQVIA, Nielsen, SAP, Sharepoint, Global ADLS. Followed the AGILE development cycle in which transformations and business rules were applied based on client requirements curating the data while loading it in the final layer to be consumed by the client for reporting and analytics. The changes were documented and deployed to the higher environments.