Analytics Engineering with DBT: Foundations of Data Transformation

Data analysts often struggle with messy, unorganized SQL scripts that lack version control, documentation, or testing. DBT solves this by bringing software engineering rigor to the data transformation layer, allowing you to build reliable and scalable data pipelines. This course guides you through the transition from writing manual queries to managing a professional analytics engineering workflow. You will gain the skills to build a robust, modular data architecture that ensures data quality and consistency across your organization. By reading through structured explanations and code examples, you will learn how to automate the repetitive parts of data modeling and focus on delivering high-value insights. What you'll learn: - Understand the core principles of analytics engineering and the role of DBT in the modern data stack - Configure DBT environments and establish connections with cloud data warehouses - Build modular SQL models using Jinja for dynamic code generation and reusable macros - Implement automated data testing and schema documentation to ensure high data quality - Manage data history and track changes over time using snapshots - Apply modern CI/CD concepts and version control to your data transformation workflows The course begins with essential terminology and foundational setup before moving into practical model building, testing strategies, and deployment best practices. You will learn to treat your data code with the same care as software, making your transformations more maintainable and transparent. This course is designed for beginner data analysts, scientists, or aspiring data engineers who have basic SQL knowledge but are new to DBT. No prior experience with analytics engineering tools is required. Start your journey into modern analytics engineering today.