DBT Analytics Engineering Certification

What is DBT-Analytics-Engineering?

The DBT Analytics Engineering Certification is a professional credential offered by DBT Labs that validates your ability to use DBT (data build tool) to transform, test, and document data effectively. It proves that you can apply software engineering best practices, such as version control, modularity, and testing, to the analytics workflow. Unlike general data engineering exams, this certification is laser-focused on analytics engineering with dbt, making it highly specialized and industry-relevant.

Who Should Take This Exam

This exam is designed for professionals working in modern data teams who bridge the gap between data engineering and data analysis. Typical candidates include:

• Analytics Engineers building and maintaining dbt projects.
• Data Analysts who want to standardize transformations and deliver cleaner, reusable datasets.
• Data Engineers aiming to strengthen their knowledge of transformation and modeling in analytics workflows.
• BI Developers and Consultants who design dashboards and need well-modeled datasets.
• Professionals with 1–2 years of SQL and analytics experience who want formal recognition of their skills.

The exam is best suited for those who already work with DBT in production or want to position themselves for analytics engineering roles.

Prerequisites and Recommendations

• Official Prerequisites: There are no strict official prerequisites. Anyone can register and attempt the exam.
• Practical Recommendations:
  • Strong SQL knowledge (CTEs, window functions, joins, subqueries).
  • Hands-on experience with dbt Cloud or dbt Core.
  • Familiarity with Git, version control, and collaborative workflows.
  • Understanding of testing, documentation, and deployment in data projects.
  • Prior certifications like Google Data Analytics Professional Certificate or Snowflake SnowPro Core can provide a good foundation.
  • At least 6–12 months of dbt project experience is highly recommended before attempting.

Exam Objectives and Domains

The dbt Analytics Engineering exam is divided into core domains that reflect practical, real-world workflows.

Project Organization and Development

• Structure dbt projects using models, seeds, snapshots, and sources.
• Apply naming conventions and folder structures.
• Use macros and Jinja for reusable transformations.

Testing and Documentation

• Implement tests to validate data quality.
• Create and manage schema.yml files.
• Document models, sources, and columns effectively.
• Ensure stakeholders can navigate project documentation.

Deployment and Version Control

• Work with dbt Cloud jobs and environments.
• Manage dbt runs in production.
• Apply Git workflows for branching, merging, and reviewing changes.

Advanced SQL and Modeling

• Build modular and maintainable models using CTEs and Jinja.
• Use incremental models and materializations.
• Handle performance optimization and warehouse-specific features.

Collaboration and Best Practices

• Follow analytics engineering conventions.
• Work in team-based environments.
• Apply CI/CD principles for analytics pipelines.

What Changed in This Version

The latest version of the dbt Analytics Engineering exam introduced:

• New Topics: CI/CD with dbt Cloud, environment management, and macros.
• Expanded Testing Section: More emphasis on custom tests and data quality assurance.
• Removed Topics: Older references to legacy dbt Cloud UI workflows were removed.
• Weight Shifts: Greater weight on project organization and documentation (to align with real-world collaboration).

Registration and Scheduling

• Register via the dbt Labs official certification portal.
• Choose online proctored delivery (no on-site testing available).
• Exams are available year-round with flexible scheduling.

Pricing and Vouchers

• Standard Price: $200 (USD).
• Regional Pricing: Some discounts may apply based on region.
• Student/Military Discounts: dbt Labs occasionally provides reduced pricing for students or military personnel.
• Voucher Programs: Employers or training partners sometimes sponsor vouchers for teams.

Policies You Should Know

• One attempt per registration; retakes require re-purchase.
• Rescheduling allowed (up to 24 hours before the exam).
• Strict identity verification required (government-issued ID).
• No external materials, dual screens, or unauthorized software.

Scoring and Results

• Scoring scale: 0–1000 points.
• Passing score: 700.
• Partial credit: Provided for multi-step and multiple-response questions.
• Results: Delivered immediately upon completion.
• Score report includes domain-level performance breakdown.

Exam Day and Test Experience

• Delivery Mode: Online proctoring only.
• Check-in: Requires ID verification and environment scan.
• Allowed Items: Only computer, mouse, and keyboard (no notes, phones, or external tools).
• Breaks: No scheduled breaks; the clock continues if you leave.
• Interface Tips: Flag questions for review; clear navigation between questions.
• Time Management: 90 minutes for ~60 questions—aim to complete first pass in 60 minutes, leaving 30 minutes for review.

Study Plan and Resources

Beginner (6 Weeks Plan)

• Week 1–2: Learn dbt basics (models, sources, seeds). Practice SQL.
• Week 3: Focus on testing and documentation. Build a small dbt project.
• Week 4: Study deployments and Git workflows.
• Week 5: Attempt practice questions and refine weak areas.
• Week 6: Take a full practice exam and review all concepts.

Experienced (3 Weeks Plan)

• Week 1: Revise dbt fundamentals; refresh advanced SQL.
• Week 2: Deep dive into tests, macros, and CI/CD workflows.
• Week 3: Practice exam + review mistakes; finalize with quick refresh of key domains.

Recommended Resources:

• dbt Labs official documentation.
• dbt Learn courses (free and paid).
• Community Slack groups.
• Third-party practice exams and study guides.

Certification Validity and Renewal

• Certification validity: 2 years.
• Renewal options: Retake the exam, or complete newer dbt certifications if offered.
• No CE credits required yet, but staying active in dbt projects is encouraged.

Career Outcomes

The dbt Analytics Engineering certification is highly valued in data-focused organizations.

Related or Next-Step Certifications

• Snowflake SnowPro Core – complements dbt by focusing on the warehouse layer.
• Google Professional Data Engineer – for broader data pipeline knowledge.
• dbt Advanced Certifications (future planned) – expected as dbt Labs expands certification offerings.

How This Exam Compares to Similar Certifications

Compared to Google Data Engineer, the dbt exam is narrower but more hands-on in analytics modeling. Unlike SnowPro Core, which validates warehouse expertise, dbt certification focuses purely on transformation and analytics best practices. It is easier in scope but requires strong practical knowledge of dbt projects.