---
title: "How to build dbt modelling with claude with Claude & Rig"
description: "Describe the model you need in plain English and Rig drafts the dbt SQL, tests and docs against your real schema, then materialises it into your warehouse. A step-by-step guide to building it yourself on your own data with Claude and Rig."
canonical: "https://rig.so/apps/dbt-modelling"
format: markdown
---

[All apps](https://rig.so/apps)

DataData model

# dbt modelling with Claude

Build and document dbt models without a data hire

Describe the model you need in plain English and Rig drafts the dbt SQL, tests and docs against your real schema, then materialises it into your warehouse.

Runs on![Snowflake logo](https://img.logo.dev/snowflake.com?token=pk_AdkTbLWDS1iunmvTK2qm1g&size=44&format=png&retina=true)![BigQuery logo](https://img.logo.dev/cloud.google.com?token=pk_AdkTbLWDS1iunmvTK2qm1g&size=44&format=png&retina=true)![dbt logo](https://img.logo.dev/getdbt.com?token=pk_AdkTbLWDS1iunmvTK2qm1g&size=44&format=png&retina=true)

[Build this on your data](https://app.rig.so/signup)[Book a demo](https://rig.so/book-demo)

## The problem

Clean, modelled tables are what make every downstream answer trustworthy, but building and maintaining dbt models needs a data engineer. Small teams either go without a model layer or wait weeks for one change.

## Built from Rig's building blocks

Rig is not a fixed template. It is a set of building blocks, and this app is one way to assemble them. Take what you need, then shape the workflow around your own pain.

Connect Snowflake or BigQuery

Context layer reads your real schema and joins

Claude drafts the dbt SQL, tests and docs

Materialise the model back into the warehouse

## How to build it

1. 1
   
   ### Point Rig at your warehouse
   
   Connect Snowflake or BigQuery read-only. Rig reads your information schema so it models against real tables, not guesses.
2. 2
   
   ### Describe the model
   
   Say what you need, 'a daily revenue fact joining orders, refunds and users', and Rig drafts the dbt SQL with tests and documentation.
3. 3
   
   ### Review the lineage
   
   Check the generated model and its lineage in plain view. Rig validates against your schema so columns and joins are real.
4. 4
   
   ### Materialise and reuse
   
   Push the model into your warehouse so every Rig answer and dashboard runs on it. Iterate in plain English whenever the business changes.

## The outcome

A maintained, documented model layer that keeps every downstream number consistent, without hiring a data engineer.

[Build your own version](https://app.rig.so/signup)

## More Data apps

[Ask for a dashboardMRR by plan, last 12 monthsBuildMRR£342kChurn2.1%EnterpriseGrowthStarterJulSepNovJanMarMayDashboardSelf-serve dashboardsLet anyone build a custom dashboard in plain EnglishBusiness teams ask for the report they need and Rig builds a governed, custom dashboard on your modelled data, so the data team stops being a ticket queue.![Snowflake logo](https://img.logo.dev/snowflake.com?token=pk_AdkTbLWDS1iunmvTK2qm1g&size=36&format=png&retina=true)![BigQuery logo](https://img.logo.dev/cloud.google.com?token=pk_AdkTbLWDS1iunmvTK2qm1g&size=36&format=png&retina=true)CleoHow to build](https://rig.so/apps/self-serve-dashboards)

---

- [This page as HTML](https://rig.so/apps/dbt-modelling)
- [Site map for language models](https://rig.so/llms.txt)
- [API and agent documentation](https://rig.so/developers)