Sonam Jadhav

Built and maintained ETL pipelines using AWS Glue, handling flat file and API ingestion. Integrated datasets from multiple sources to ensure data consistency and reduce complexity. Enhanced Amazon Aurora PostgreSQL performance by implementing pg_partman-based table partitioning. Implemented AWS Lambda functions to perform serverless transformations and trigger workflows, improving scalability and reducing infrastructure overhead. Used AWS Step Functions for orchestrating modular, maintainable data workflows. Resolved production issues to ensure minimal downtime in pipelines.

Azure Databricks & ADF: Developed scalable ETL solutions using Azure Databricks and Azure Data Factory. Designed optimized Apache Spark workflows via Databricks notebooks to reduce runtime. Implemented Delta Lake for transactional reliability and version control in data lakes. Automated data workflows, reducing manual intervention by 40%. Mentored junior engineers on Spark and Databricks best practices. EDW Data Modernization Project: Developed cloud data pipelines with Informatica IICS and Push-Down Optimization. Migrated data from Teradata, FTP, and Oracle to Snowflake Data Warehouse. Implemented SCD Type 1 & 2 logic. Refactored Teradata views and ported logic to Snowflake. Implemented CI/CD for deployments across Dev → SIT → Test → Prod. Resolved defects across IICS taskflows and Snowflake views to ensure data accuracy.