Mahanth Sai

Collaborated with stakeholders to translate reporting needs into data models, ETL/ELT pipelines, and enterprise Power BI solutions in a lakehouse environment. Built Power BI semantic models and dashboards with security, incremental refresh, and performance optimization techniques. Created reusable dataflows and orchestration workflows for event-driven refresh and governed dataset publishing via CI/CD and workspace governance.

Built scalable cloud ETL/ELT and analytics solutions for financial and regulatory datasets using AWS batch and streaming services. Developed metadata-driven pipelines with PySpark and orchestration, and engineered distributed processing on EMR for performance and reliability. Optimized Redshift and Snowflake warehouses using tuning strategies and automated deployments with Terraform, CI/CD tooling, and infrastructure governance.

Designed ETL/ELT pipelines to ingest and transform clinical, claims, pharmacy, and provider data from multiple sources into Azure-based lakehouse and warehouse models. Implemented distributed PySpark transformations to improve data quality and reduce batch processing time, and set up near real-time ingestion using Azure services. Supported Power BI semantic modeling with security and incremental refresh, and delivered automated deployment and monitoring via Azure DevOps and CI/CD.