I'm leading a configuration-driven anomaly detection platform for HCL America and Comcast on Databricks, processing more than 3 million records daily across business domains. The platform investigates unfamiliar failure patterns, automates root-cause analysis, and surfaces actionable anomaly trends through Metric Views and dashboards.
Previously at Providence, I owned data engineering for employee timesheet validation across 2 million records spanning timecards, Azure DevOps work items, tasks, and user stories. I optimized more than 10 production Spark pipelines to reduce average runtime by 20% and built reporting pipelines that contributed to a 25% improvement in Agile sprint throughput.
I also redesigned Azure ingestion and orchestration workflows using API extraction, parallelized Azure Data Factory pipelines, and automated data-quality testing across 150+ business metrics. That work reduced pipeline runtime by 67% and compute costs by 30%.
I work across Python, PySpark, SQL, Databricks, Azure, CI/CD, and Power BI, combining platform engineering with practical analytics. I've also built a reusable Power BI visual with TypeScript and D3.js that doubled user engagement and supported carbon-emissions reduction initiatives.
