# dbt Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/dbt
> Markdown URL: https://aitinkerers.org/technologies/dbt.md
> Technology record last updated: 2026-03-18T21:54:44Z
> Generated: 2026-09-21T16:51:36Z

dbt (data build tool) applies software engineering rigor (testing, version control) to analytics, transforming raw warehouse data via SQL.

dbt is the modern standard for data transformation: it enables analytics engineers to build modular, maintainable data products directly in the cloud data warehouse. You define all transformations using simple SQL `SELECT` statements, centralizing business logic for clarity and collaboration. The framework enforces engineering best practices: specifically, Git for version control, automated testing to ensure data quality, and documentation generation for every model. This process delivers reliable, governed data pipelines at scale, moving analytics teams from raw data to trusted outputs with confidence.

- Official technology site: https://www.getdbt.com
- Public AI Tinkerers demos and talks: 2
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Accurate vibe analytics](https://nashville.aitinkerers.org/talks/rsvp_TEGj2gbUeIQ)

Dbt-skillz is a project I open sourced that "compiles" a dbt project into a claude code compatible skill. We use it in our CI/CD workflow to always stay fresh. We use it to (1) build accurate BI/reporting dashboards, and (2) review them with a review-agent workflow that uses the skill.

- Event context: AI Tinkerers Nashville – Software Development in the Age of AI — 2026-03-25 — Nashville
- Public talk page: https://nashville.aitinkerers.org/talks/rsvp_TEGj2gbUeIQ

### [Teaching an AI Agent to Build a Data Warehouse](https://denver-boulder.aitinkerers.org/talks/rsvp_psJLyTsUVD8)

This talk presents Modlr, a prototype AI agent that automates the design and management of data warehouses, one of the most complex and error-prone tasks in data engineering. Modlr uses Claude Code as its main agent, orchestrated with FastMCP and LangGraph, to reason about structured business context, enforce modeling conventions, and iteratively build production-ready dimensional models and semantic layers. During the talk, I will demonstrate the agent creating a complex warehouse model live—transforming a task that normally takes weeks into under two minutes—and show visualizations via Mermaid lineage and ERD diagrams, immediately deployable to dbt and MetricFlow for querying via SQL or natural language. I will walk the audience through the code, system architecture, technology stack, and validation mechanisms that make this possible.

- Event context: AI Tinkerers Denver Meetup - November 24, 2025 — 2025-11-25 — Denver
- Public talk page: https://denver-boulder.aitinkerers.org/talks/rsvp_psJLyTsUVD8

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