At Fractal Analytics, I design metadata-driven ETL/ELT pipelines with Azure Data Factory and Azure Databricks for Procter & Gamble and AB InBev. I build PySpark transformations across Bronze, Silver, and Gold layers for forecasting workloads.
I optimized joins, partitioning, and transformations to improve Spark pipeline execution time by roughly 30–50%. I also created reusable notebook frameworks for model orchestration, retraining, and best-model selection.
At Verificient Technologies, I built backend REST APIs for a Customer Success Management dashboard and contributed to role-based dashboards and experiment management APIs. I also shipped an AI support chatbot using OpenAI, LangChain, and RAG to automate resolution of common queries.
I work with Azure data platforms, Delta Lake, Unity Catalog, and Power BI, and apply Databricks Genie and BMAD in AI-assisted engineering workflows. I hold Microsoft Certified: Azure Fundamentals (AZ-900) and am working toward Azure DP-203 and Databricks Certified Data Engineer Associate.
