Brent Burgess

Brent Burgess

Data leader who ships AI automation.

21 years in data. Most recently Director of Data and Analytics at Wine.com: a team of 7, a governed lakehouse at 99.9% uptime and production AI agents.

Open to full-time director of data, head of data and principal data architect roles, Bay Area or remote.

Résumé Download PDF (2 pages, 162 KB)

Email brent@brentburgess.com or message me on LinkedIn, or ask a question about my work.

At Wine.com, 2017–2026

Lead Data Architect to Director of Data and Analytics

  1. 99.9% uptime across five years A governed lakehouse on AWS and Databricks that replaced an on-premise SQL Server estate, domain by domain.
  2. One certified metric layer An owner, grain and lineage on every definition, and finance, operations, product and marketing all reported from it.
  3. Production AI agents Answering from certified metric definitions through MCP servers behind SSO and OAuth, plus a Claude Code suite that engineers and analysts used daily.
  4. A team of 7 Across data engineering, ML and analytics, with data standards adopted across more than 8 engineering teams.

Before Wine.com: lead data architect at UC Berkeley’s Graduate School of Education, and for 5 client organizations as a consultant. Full résumé

“Brent combines the most important traits in a data leader - business acumen with technical chops.”

Derek Stevenson, who managed Brent at Wine.com as VP of Data

“He is thoughtful, steady, and experienced — the kind of person who can bring order to messy systems without creating unnecessary drama along the way.”

Greg Tatem, who as Wine.com’s CTO hired Brent in 2017 and managed him until 2022

“Brent was not only my manager but also a mentor who played a pivotal role in my professional development.”

Surya Kasireddy, who was Brent’s first hire as a manager, and a senior data engineer on his team at Wine.com for about four years

The platform I led at Wine.com

More than 20 source systems fed one governed lakehouse and one set of certified metrics, which the business teams, the forecasts and the AI agents all read from.

  1. More than 20 source systems
  2. A governed lakehouse 99.9% uptime across five years
  3. One certified metric layer an owner, grain and lineage on every definition
  4. Read by the business and its AI agents finance, operations, product and marketing; forecasts and alerts; AI agents through MCP servers

Still building

AI and Python systems I built and run in public, each with a case study.

Latest writing: A sample audit plan: a small site’s analytics with promises to keep,