Neeraj Paruchuri

– Architected core ETL pipelines with Python and PySpark to stabilize high-volume data ingestion workflows. – Optimized legacy daily run logics, slashing pipeline runtime from 8 hours down to 15 minutes. – Deployed an algorithmic fuzzy matching engine to accurately map external vendor entities with internal IDs. – Secured data pathways and restricted privileges using UNIX operations to enforce strict client data security. – Executed automated data validation and schema verification via Hive SQL across stagi

∗ Optimized internal data management workflows, reducing processing and pipeline execution times by 30%. ∗ Engineered automated routing logic into the CRM, slashing execution times and saving $3,150 in overhead. ∗ Established standardized data quality frameworks and validation procedures to ensure reporting accuracy. ∗ Engineered central analytics dashboards to monitor data integrity and track operational infrastructure KPIs.