DBT for Data Engineers

In this course, you will be introduced to the modern ELT approach and how dbt fits into the modern data stack compared to traditional ETL. You will quickly move from concepts to hands-on practice by building your own dbt project. You’ll learn how dbt helps you structure SQL transformations, add tests, and automatically generate documentation. We will cover the differences between dbt Core and dbt Cloud, how to collaborate using Git, and how dbt integrates into CI/CD workflows. Through practical exercises, you’ll build an end-to-end data model and gain insight into how larger organizations manage their analytics pipelines with dbt.

What you learn

• The fundamentals of dbt and the modern ELT approach.

• How to use dbt to structure and test SQL transformations.

• Automatically documenting and visualizing your data models.

• Working with Jinja, macros and references between models.

• How to integrate dbt with Git and CI/CD.

After this course you will be able to:

• Independently set up and manage a dbt project.

• Build SQL models with version control, testing and documentation.

• Ensure data quality using dbt tests.

• Integrate data analysis and data engineering workflows into a single process.

For whom

• Data engineers and analytics engineers.

• BI specialists.

• Data analysts who want to professionalize and automate their SQL transformations.

Prerequisites

• Basic knowledge of SQL.

• Some familiarity with data warehouses or data modeling is a plus.

Content (global program)

Part 1

• Introduction to the modern data stack.

• The difference between ETL and ELT, and the role of dbt.

Part 2

• Your first dbt project.

• Setting up, configuring and running models.

Part 3

• Testing and documenting.

• Data quality, docs and lineage.

Part 4

• Advanced features.

• Macros, Jinja and model dependencies.

Part 5

• Integration with Git & CI/CD.

• Deployment and collaboration.

Part 6

• Best practices & Q&A.

• Patterns, tips and next steps.