Hemanth Abbina

Built data models and scalable ETL/ELT pipelines with PySpark for healthcare claims and related datasets to support analytics and ML workloads. Developed reusable feature pipelines with data scientists and automated ML workflows including experiment tracking and model versioning using MLflow and CI/CD practices. Implemented RAG data pipelines with LangChain and vector indexing, and added data quality validation, monitoring, and Spark performance optimizations to improve reliability and reduce processing time.

Designed data models and built scalable ETL/ELT pipelines using Python, PySpark, and SQL to integrate core banking, transactional, and customer data into an enterprise warehouse. Implemented batch and incremental (CDC) workflows for revenue and regulatory reporting, credit risk analysis, and BI consumption. Optimized complex SQL objects and ETL/Spark jobs to reduce runtimes, eliminate redundant processing, and meet SLA requirements while supporting data governance with validation checks and audit trails.

Analyzed large-scale healthcare and pharmaceutical datasets using SQL and Python to find trends and operational insights. Built and maintained interactive dashboards and reports in Power BI and Tableau for KPIs across manufacturing and quality-related functions. Automated recurring data extraction and validation, performed exploratory data analysis, and supported reporting and root-cause investigations by transforming data from ERP/LIMS/CRM and relational databases.