Introduction to Analytics Engineering Workflows with dbt

As data volumes grow, businesses need clean, structured, and reliable data to make critical decisions. Transforming raw warehouse data into analysis-ready tables is the core challenge of modern analytics engineering. This text-based course guides you through the fundamentals of analytics engineering using dbt. You will learn how to write modular SQL, implement version control, and build robust data pipelines that turn messy data sources into trusted business insights. What you'll learn: - Understand the role of an analytics engineer and the core concepts of the modern data stack. - Write modular, reusable SQL models using dbt to transform raw warehouse data. - Apply testing frameworks to validate data quality and ensure pipeline reliability. - Configure documentation and dependency graphs to keep your data models clear and maintainable. - Implement version control and collaborative workflows to manage changes safely. You will start by mastering foundational terminology and basic setup concepts before moving on to practical written exercises. Through clear text explanations and structured code examples, you will practice structuring models, running tests, and generating documentation. This course is designed for beginner data analysts, aspiring analytics engineers, and database administrators looking to modernize their data transformation workflows. No prior experience with dbt is required, though a basic understanding of SQL is helpful. Start building cleaner, more reliable data pipelines today.