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DATA ENGINEERING SERIES

Transform Data with DBT + MCP

Build analytics pipelines by chatting with your IDE

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Transform Data with DBT + MCP project preview
dbt
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Difficulty

Beginner

Time to complete

90 minutes

Availability

Free

BUILD

What you'll build

Create production-ready data transformations with DBT. Build customer analytics, add data quality tests, and generate documentation - all through natural language in Cursor.

1. Initialize DBT project via chat

Set up your DBT project structure and connect it to PostgreSQL. Configure the DBT MCP server so Cursor can run transformations through plain English

2. Create customer analytics model

Write SQL that transforms raw orders into business insights. Calculate lifetime value, segment customers by behavior, and identify their favorite product categories

3. Run transformation by chatting

Tell Cursor to build your analytics table and watch it happen. DBT compiles your SQL, executes it against the database, and shows you instant results

4. Add automated quality tests

Add schema tests that catch data issues before they break dashboards. Test for nulls, duplicates, and invalid values - the same patterns used by Airbnb and GitLab

5. Generate interactive docs

Create professional documentation with lineage diagrams. See exactly how data flows through your pipeline and share it with your team

Your portfolio builds as you work.

Every project documents itself as you go. Finish the work, and your proof is ready to share.

PROJECT

Real world application

Skills you'll learn

  • DBT Transformations

    Transform raw data into analytics-ready models with SQL

  • SQL Analytics

    Master CTEs, aggregations, and window functions

  • Data Quality Testing

    Schema tests that catch issues automatically before they break dashboards

  • Data Lineage

    Visualize how data flows through your transformation pipeline

  • Auto Documentation

    Generate professional docs from your models and schemas

  • Production Pipelines

    Same tools used by data teams at Airbnb and GitLab

Tech stack

  • dbt logo

    dbt

    Transform data with SQL, test automatically, document everything

  • Cursor logo

    Cursor

    The AI-powered IDE that runs your transformations through chat

Huge thanks to NextWork for all the awesome hands on projects. I have done 17 so far and learned so much. Keep up the amazing work!

Jonathan Goodenough

NextWork Student

OUTCOME

Where this leads.

Relevant Jobs

Roles where these skills matter:

  • Data Engineer
  • Analytics Engineer
  • BI Developer
  • Data Analyst
  • Platform Engineer

Data Engineering with MCPs

Continue your data engineering journey - build real-time pipelines, analytics dashboards, and production data systems

Data Engineering with MCPs

Continue the Journey

FAQs

Everything you need to know

No prior SQL experience required. This project teaches you SQL concepts like CTEs, aggregations, and window functions as you build. You will write real queries, but Cursor helps translate your plain English into working SQL. By the end, you will understand how production data teams transform raw data into business insights.

SQL is the language you use to write database queries (like SELECT * FROM customers). DBT is a framework that helps you organize, test, and document those SQL transformations. Think of it this way - SQL is the language, DBT is the toolkit. With raw SQL files, you would manually run queries and remember dependencies. With DBT, you get automatic dependency management, built-in testing, and documentation - all organized as code.

Most learners complete this project in 60-90 minutes. The project has 3 main steps - setting up DBT and MCP servers (20 minutes), building and running your customer analytics model (25 minutes), and adding data quality tests (15 minutes). The Secret Mission (documentation and lineage visualization) adds another 20-30 minutes. You can work at your own pace. If you get stuck, join the NextWork Discord community for peer support.

You will build a complete customer analytics model that transforms raw e-commerce data into business insights. Your model calculates customer lifetime value, segments customers by behavior (inactive, occasional, regular, VIP), and identifies each customer's favorite product category. You will also add production-grade tests that automatically catch data quality issues, and generate professional documentation with interactive lineage charts. This is portfolio-worthy work that demonstrates real analytics engineering skills.

Companies like Airbnb and GitLab do not just transform data - they test it. Without tests, bad data silently breaks dashboards and reports. A NULL value in customer lifetime might cause calculations to fail. Duplicate customer IDs could inflate metrics. DBT tests catch these issues automatically before they reach stakeholders. In this project, you will add not_null, unique, and accepted_values tests - the same patterns used by production data teams worldwide.

One Project. Real Skills.

90 minutes from now, you'll have completed Transform Data with DBT + MCP. No prior experience needed. Just step-by-step guidance and a real project for your portfolio.

Let's Build This

Beginner-friendly