NITESH PANDIT

Servicer Data Onboarding (SDO): Designed and developed a scalable Databricks data onboarding framework using Medallion Architecture (Bronze–Silver–Gold) to process and standardize multiple servicer data feeds. Built end-to-end PySpark ETL pipelines to automate ingestion, transformation, validation, and reconciliation of large-volume financial data. Implemented data quality, schema validation, and logging frameworks, ensuring traceability and consistent data standards across environments. Automated reconciliation and financial reports via Databricks notebooks and Python, reducing manual reporting effort by about 80%. Orchestrated workflows using Azure Data Factory and JAMS Scheduler for reliable sequencing, failure handling, and SLA tracking. Optimization & Platform Modernization: Tuned Spark configurations, caching, and partitioning in Databricks, improving job execution time and cluster efficiency by up to 40%. Migrated 100+ Databricks jobs to Unity Catalog, strengthening governance, access control, and environment consistency. Led end-to-end testing of 100+ production jobs ahead of migration to Databricks Runtime 14.3 LTS, validating dependencies and performance stability. Enhanc