Analytics Engineering Workflows with dbt

This course helps you advance your skills in analytics engineering and gives you the practical abilities required to build scalable and reliable dbt projects. You will begin by strengthening your understanding of reusable SQL development with Jinja and macros and learn how to organize transformation logic for large data systems. From there, you will explore incremental models, snapshots, testing strategies, documentation practices, and core observability concepts that support trustworthy analytics workflows. The course concludes with collaboration techniques and workflow automation, where you will implement Git based version control, continuous integration pipelines, and scheduled dbt jobs. With a practical and applied approach, the course covers advanced concepts such as creating modular logic with macros, optimizing performance with incremental processing, structuring projects into clear layers, validating models with schema and custom tests, managing metadata, and reviewing lineage in dbt Docs. You will learn how to maintain clean project organization, implement testing and documentation standards, analyze run results and logs, and support production ready automation in modern analytics environments. By the end of this course, you will be able to: • Build reusable SQL logic using Jinja and macros • Design and implement incremental and snapshot models • Refactor dbt projects to maintain a clean and well organized DAG • Create, run, test, and document advanced dbt models • Apply testing, documentation, and observability practices to ensure data quality • Collaborate using Git and review workflows for dbt development • Configure continuous integration pipelines for automated model validation • Schedule and monitor dbt jobs for reliable production execution This course is designed for aspiring analytics engineers, data engineers, BI developers, and SQL practitioners who want to expand their skills in advanced dbt practices, data quality frameworks, collaborative work