• Built and containerized ETL pipelines for live event data ingestion, reducing manual workload by 30%.• Automated monitoring and metadata entry, improving pipeline reliability and reducing manual intervention.• Developed automated data quality management processes using DBT, ensuring consistent data accuracy.• Achieved 99.99% uptime for pipelines ingesting streaming and transactional data from multiple sources.• Managed end-to-end machine learning pipelines using Docker, AWS SageMaker, and other cloud tools.

• Led the successful migration of data models from Redshift to Snowflake, ensuring minimal defects and seamless transitions.• Automated key testing and development workflows, significantly reducing manual effort and improving efficiency.• Worked closely with clients to understand their needs and design data models that achieved high customer satisfaction.• Modernized legacy code and enhanced data migration processes, improving overall system performance.• Streamlined file transfer processes using cloud storage and automation tools, reducing manual intervention by a large margin.• Applied ETL tools to optimize data integration, enhancing processing speed and accuracy.

• Enhanced Data Accuracy: Tested and fixed models using DBT and DataOps pipeline, improving data accuracy by 40% and reducing testing time by 50%.• Optimized Data Quality Assurance: Applied complex SQL logic and 20+ test cases across different model dimensions, ensuring high-quality data delivery.• Resolved Critical Bugs: Fixed over 200+ bugs in two quarters, preventing production escalations and maintaining SLA compliance.• Innovative Testing Strategies: Implemented testing techniques like boundary value analysis, equivalence partitioning, and decision table testing to validate model data.

Worked on sugarcane plant species to glean immense salt resistance in order to grow even at high saline conditions and explored the genetics of sugarcane varieties.