Bhargavi Vempuluru

Designed and implemented end-to-end ETL pipelines for payroll and compliance analytics using Python, SQL, PySpark, and Apache Spark on Azure Databricks, processing large-scale employee and financial datasets. Built and maintained analytics-ready datasets in Azure Data Lake Storage Gen2 following lakehouse principles, enabling accurate salary processing, statutory reporting, and downstream business analytics. Optimized PySpark and SQL transformations in Azure Databricks, reducing query execution and processing time by 25–45% on high-volume payroll workloads. Managed data access, governance, and schema consistency across several payroll domains by utilizing Databricks Unity Catalog. Built rerunnable SQL procedures to support schema changes and deployment-safe releases with minimal impact on payroll processing. Created Databricks dashboards for internal data quality monitoring, reconciliation, and mismatch detection between source and target payroll systems. Collaborated closely with business leaders, payroll SMEs, and cross-functional Agile teams to translate business and compliance requirements into reliable, production-ready data solutions.