Data, BI, ML, & AI: from architecture to outcomes, but only the stuff that actually made it to production.

Quick Background: A technology executive with 20+ years architecting cloud data and AI platforms for Fortune 500 clients from healthcare to financial services to manufacturing.

The lines between Data, BI, ML, & AI disciplines are not as clean as the org charts suggest. Good data engineering is what makes generative AI trustworthy instead of just impressive. You cannot build a reliable AI agent on top of a data platform nobody governs. And analytics doesn’t mean much anymore if it’s stuck in a static dashboard while everyone else is asking questions in natural language. I have spent my career living at that intersection by architecting the Snowflake, Databricks, and Fabric platforms underneath it all, then using that same foundation to build the AI-assisted tooling that speeds up the work itself. This blog is where I write about that combination of the pipelines, the models, and the BI layer actually come together in production, and not as three separate disciplines, but as one system that has to work end to end.

Recent Certs: Claude Certified Architect · AWS Certified Generative AI Developer · SnowPro Core · dbt Analytics Engineering