At Diebold Software Services, I engineer and maintain data pipelines for MUFG financial regulatory reporting, processing transaction and trade data from multiple source systems. I implement regulatory business rules in PySpark and SQL, with lineage, validation checks and audit trails to support traceability.
I designed and implemented a Bronze/Silver/Gold prototype using Azure Data Factory, Azure Databricks and Delta Lake. I also optimized Databricks notebooks and PySpark jobs, reducing manual reporting effort by about four hours per week.
At HCLTech, I developed Python and PySpark ingestion and ETL pipelines, modeled incident history in relational databases, and prepared datasets for machine learning use cases. I also built a log analytics pipeline that reduced repeat incidents by approximately 25%.
