15+ years building data infrastructure at Workday and Lyft — real-time pipelines, growth analytics at scale, and self-healing DataOps. I built the analytics stack that measured multi-million-dollar acquisition campaigns at Lyft. Now I'm building agentic systems that keep data infrastructure reliable at streaming scale.
The problems I've solved at Lyft and Workday map directly to what streaming platforms need: real-time event pipelines, audience segmentation at scale, growth attribution that holds up under scrutiny, and self-healing infrastructure that doesn't page your on-call at 3am.
At Lyft I built the growth analytics stack — the data systems that measured rider acquisition, attribution windows, and marketing ROI across multi-million-dollar campaigns. The same infrastructure patterns apply to content performance, subscriber acquisition, and engagement scoring. At Workday I shipped an AI Companion that answers natural-language questions about marketing performance, grounded in a governed semantic layer — exactly the kind of query interface content analytics teams are building.
My Real-Time Retail Sales Pipeline project is a production-grade streaming system: Kafka ingestion, PySpark transformations, Snowflake + dbt modeling, Airflow orchestration. I know how to build data infrastructure that runs at the speed content decisions demand.
I'm actively talking to teams building the next generation of streaming analytics, content intelligence, and self-healing data infrastructure. I know how to ship at the velocity that content teams need.