Technical PM · Data Infrastructure · Analytics · ML Platforms
I build the data infrastructure and analytics products that companies run on — from warehouse architecture and ML pipelines to API-first platforms that generate measurable revenue.
Domain focus
I'm a Technical Product Manager with a Computer Science degree and a builder's instinct — which means I don't just work alongside data and engineering teams, I speak their language fluently. I've owned product roadmaps for warehouse re-architecture, streaming pipelines, semantic layers, data governance, and ML deployment infrastructure. When I write a spec, I've already stress-tested the technical assumptions behind it.
My career spans founding PM at a pre-product SaaS startup, managing 10+ M&A integrations across $80M+ ARR at a global software company, and currently leading data platform strategy at Mavrck — where I operate at the intersection of three teams: Analytics & Reporting Engineering, Data Engineering, and Internal Data Analysts. Owning data products end-to-end — from infrastructure to insight — is where I'm most at home. Every context has been different. The rigor has been the same.
I hold an M.S. in Information Systems from the University of Maryland's Robert H. Smith School of Business and a B.Tech in Computer Science from NMIMS, India. I'm certified in AI for Product Management, Scrum Product Ownership, and Digital Product Management. My fluency spans the full stack of data work — from warehouse architecture and pipeline design to BI tooling, ML deployment, and stakeholder alignment. I move between an engineering design doc and an executive business review without losing the thread in either direction.
Four problems, four different contexts. Each one required shipping something technically complex under real constraints — and leaving a measurable mark.
I enjoy conversations with founders, operators, and product leaders working on hard data and infrastructure problems. If something here resonated — or you're navigating a data platform, analytics, or ML productization challenge — reach out.