The course teaches how to implement schema tests, write custom SQL tests, configure severity levels, and validate business logic. You will learn how to interpret test results, diagnose failures, and use testing artifacts to improve data reliability and trust.

Yes. You will learn how to maintain rich metadata using YAML, document models and columns, define ownership and exposures, and generate dbt Docs. The course emphasizes building documentation that supports team collaboration and long-term maintainability.

Yes. You will learn how to implement freshness checks, monitor SLAs, read dbt run results and logs, and produce summary reports. These skills help you understand pipeline health, detect failures early, and improve production observability.

Yes. The course introduces Git fundamentals, branching and merging strategies, and pull request workflows specifically for dbt projects. You will practice collaborating on shared repositories, reviewing changes, and maintaining version history.

Yes. You will learn how to configure CI workflows using GitHub Actions to automatically run dbt builds and tests on commits. The course demonstrates how CI helps prevent broken models and ensures data quality before changes reach production.

Yes. You will learn how to automate dbt runs using scheduling tools such as dbt Cloud or cron jobs. The course also covers monitoring scheduled jobs, verifying successful runs, and managing alerts and notifications.

By the end of the course, you will be able to build scalable and maintainable dbt projects, implement advanced transformations, ensure data quality through testing and observability, collaborate effectively using Git, and automate analytics workflows using CI and scheduling.

Yes. This course assumes prior knowledge of SQL and foundational dbt concepts such as models, refs, sources, and basic testing. It is intended as an advanced course that builds on introductory analytics engineering skills.

The course typically takes four to five weeks to complete, with an average workload of three to four hours per week. Actual completion time may vary depending on your experience with dbt, Git, and automation tools.

Yes. Upon completing all modules, practice assignments, and graded assessments, you will receive a certificate of completion demonstrating advanced dbt development and workflow automation skills.

This course supports roles such as Analytics Engineer, Senior Data Analyst, BI Engineer, Analytics Platform Engineer, and Data Engineer working on analytics infrastructure. The skills are especially relevant for teams operating dbt in production environments.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.