Implemented an end-to-end data warehousing solution for a client using Azure, ensuring efficient storage, processing and retrieval of large volumes of data along with implementing optimized data pipelines to streamline data ingestion and transformation.Designed and developed a utility facilitating the scheduling of pipelines on a custom data lake platform, leveraging Azure Data Factory and Airflow technologies.Automated the extraction of data from sources like Oracle, Redshift, and S3, and implemented a data warehousing solution including end to end data ingestion and transformation for a leading pharmaceutical client using AWS.Implemented data ingestion solution to efficiently extract, transform and load data for a ZS Internal Asset and efficiently comprehended the stakeholders business and domain requirements to create final data products.Spearheaded the end-to-end implementation of a custom orchestrator for the ZS MLOPs tool.Developed utilities using Python to extract data from diverse sources such as SQL Server, MySQL, ADLS, S3, and more.Worked as a core team member of the API development team for ZS Datalake framework.