A 20-minute hands-on lab where you'll use dbt Wizard to investigate a real retail dataset, extend an existing model with a new source, and fix a broken pipeline - all without writing SQL by hand or breaking downstream models.
Watch a working example before you walk in the door. You'll do this one first on Monday.
Watch the onboarding scenario play through end-to-end. The other four scenarios will be linked individually below.
dbt Wizard is an AI agent built from the ground up for the way analytics engineers actually work. Not just code generation, but the entire data lifecycle: asking questions, investigating, understanding, changing, validating, and shipping. It's grounded in real dbt project context: lineage, tests, contracts, and defined metrics. And it's available wherever analytics engineering work actually happens.
A conversational workspace in the dbt platform, plus an embedded assistant inside dbt Studio. Tailored capabilities ship in dbt Canvas and dbt Insights for visual modeling and analytics work.
A terminal-native agent for local development. Runs against your dbt project on your laptop, with your warehouse credentials, in the editor you already use. This is what the HOL uses.
The shift isn't "AI writes SQL for you." It's that the agent understands your project, respects your lineage, and validates its own work before handing it back.
Ten capabilities that distinguish a purpose-built AE agent from a generic code assistant.
Investigate, change, validate, and ship from one surface.
Lineage, contracts, semantic definitions, tests, governance - all in context.
Reads your project the way dbt itself reads it. No translation layer.
Edits across models, schema files, and tests in a single coherent change.
A decade of analytics-engineering knowledge maintained by dbt Labs and the community.
Built-in comparison loop runs your changes against expected outputs before handoff.
You stay in control. The agent proposes; you approve.
Reasoning and tool calls are visible. Nothing happens in a black box.
Purpose-built for AE workflows, not a generic chatbot in a costume.
Snowflake, BigQuery, Databricks, Redshift, and others.
Each scenario is a self-contained workflow that takes roughly 10-15 minutes. Start with onboarding, then pick whichever scenarios match your role. The choose-your-path scenarios let you pick from three real business situations.
New AE at The Builder Depot - get a tour, understand the project, ship a first model.
Find which stores got the wrong stock allocation before the big sale.
Choose-your-path x 3: Customer 360, Operations, or Merchandising.
Build a 180-day customer activity layer and segment VIPs, big spenders, and category-loyal customers.
Reproduce a failure, find the blast radius, fix the alias chain, re-run to green. Choose-your-path x 3.
git clone https://github.com/fivetran-jacklowery/dbt_wizard_hol.git"What really sets the dbt Developer Agent apart is its precision in identifying and resolving bottlenecks. It doesn't just suggest code; it understands our existing tests and lineage well enough to troubleshoot issues almost instantly. This has significantly reduced our build times and allowed us to scale our dbt project with total confidence in our data's trustworthiness."
"We went from about 60 conformance errors to 7, using the Fusion migration agent. That's the difference between too hard and actually doable."
Install dbt Wizard, clone the repo, set up your venv, validate your environment.
A one-page printable cheat sheet of the exact prompts to use during the lab.
How the warehouse, schemas, and per-attendee credentials are wired up.
The full dbt project, seed data, scripts, and lab assets used for the HOL.