Analytics Engineering with dbt

This course equips you with practical analytics engineering skills focused on preparing, transforming, optimizing, and visualizing data using dbt. You will begin by reviewing and refactoring existing dbt models to ensure consistency, remove redundant transformations, and organize logic into clean and maintainable layers. As you move forward, you will apply standardized cleaning patterns, implement reusable macros, and enforce data quality using dbt tests. You will also design and extend business KPI models that support executive-level analytics. Next, you will deepen your understanding of performance tuning by analyzing execution plans, optimizing joins and filters, and evaluating model materializations for speed, cost, and reliability. You will learn how to improve pipeline observability by interpreting dbt logs, reviewing artifacts, managing failures, and applying freshness and SLA concepts to ensure trustworthy production workflows. The final part of the course focuses on visualization and insight delivery. You will connect dbt outputs to a BI tool, configure datasets, build dashboards based on KPI models, design executive-ready reports, automate refreshes, and share insights in a way that supports data-driven decision making across the organization. With a hands-on and applied approach, the course teaches you how to standardize transformation logic, build modular KPI models, optimize performance, monitor pipeline health, integrate analytics outputs into BI platforms, and deliver insights with clarity and impact. You will develop the ability to maintain clean project organization, implement efficient transformations, and support end-to-end analytics workflows. By the end of this course, you will be able to: • Review and refactor dbt model dependencies to maintain a clean and efficient DAG • Standardize data cleaning using reusable macros and validation strategies • Build KPI models and multi-layered business transformations • Analyze query performance and apply optimization techniques • Choose and configure dbt materializations for different performance and cost requirements • Monitor and maintain pipeline reliability using logs, artifacts, and freshness rules • Connect dbt outputs to BI tools and prepare datasets for dashboarding • Build KPI dashboards and automate reporting workflows • Communicate insights effectively through well-designed reports and storytelling techniques This course is designed for analytics engineers, data engineers, BI developers, and SQL practitioners who want to deepen their skills in dbt development, reusable SQL design, data quality practices, and workflow automation. It is ideal for learners seeking to build scalable, reliable, and well documented analytics pipelines using modern engineering workflows.

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 workflows, and automated transformations. It is ideal for anyone seeking to build dependable, scalable, and well documented analytics pipelines in modern data environments.