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dbt + Snowflake: Complete Analytics Engineering Course
New
Rating: 4.2 out of 5(19 ratings)
117 students

dbt + Snowflake: Complete Analytics Engineering Course

Raw to production: layered models, testing, incremental loads, snapshots, CI/CD, and cost control on Snowflake.
Created bySnowbrix Academy
Last updated 9/2026
English
English [Auto],

What you'll learn

  • Set up a dbt project connected to Snowflake with profiles, environments, and version-controlled configuration.
  • Build a layered model architecture with staging, intermediate, and marts layers using ref() chains.
  • Write and run dbt tests that catch data quality issues before they reach dashboards.
  • Implement incremental models that process only new or changed rows, cutting compute costs by 80 percent or more.
  • Use dbt snapshots to track slowly changing dimensions with Type-2 history automatically.
  • Write Jinja macros and use dbt packages to eliminate repetitive SQL across your team.
  • Deploy dbt in production with CI/CD, slim builds, and environment promotion workflows.
  • Apply advanced Snowflake-specific patterns including dynamic warehouse sizing, query tags, and zero-copy clones.
  • Design metrics with the dbt Semantic Layer, enforce model contracts and versioning, and prepare for the dbt Analytics Engineering Certification exam.

Course content

23 sections • 121 lectures • 8h 20m total length

  • The Analyst Who Wrote 47 Stored Procedures4:31
  • What dbt Actually Is — And What It Is Not4:42
  • dbt Projects on Snowflake — The Third Deployment Option3:19
  • How dbt and Snowflake Fit Together5:18

Requirements

  • Comfortable writing basic SQL (SELECT, JOIN, GROUP BY) — you don't need prior dbt experience.
  • A free Snowflake trial account (30 days, no credit card) — setup is covered in Module 2.
  • A computer that can run Python 3.8+ and a terminal (Windows/Mac/Linux all work).
  • No prior data engineering job experience required, but familiarity with a data warehouse concept helps.

Description

You will build production analytics pipelines using dbt and Snowflake, from your first model to a fully tested, incrementally loaded, CI/CD-deployed stack. Every concept comes with real SQL and YAML you can run immediately — not slides, not pseudocode.

This course follows Priya, a senior analyst who inherited 47 undocumented stored procedures and a Monday-morning Slack message that never stops: "the revenue dashboard is wrong again." You'll watch her (and Lumen Retail, the running case study threaded through all 22 modules) replace that fragile pipeline with a version-controlled, tested, documented dbt project — and then build your own.

You'll start from zero: installing dbt-snowflake, configuring `profiles.yml` with key-pair authentication, and running your first model. From there, the course goes deep on everything a working analytics engineer actually needs — the layered model architecture (staging, intermediate, marts) built around `ref()`, a full testing pyramid (generic tests, singular tests, source freshness), incremental models that cut compute cost by 80%+, snapshots for slowly changing dimensions, Jinja macros and packages to eliminate repeated SQL, the dbt Semantic Layer, model contracts and versioning for safe breaking changes, CI/CD with GitHub Actions and Slim Builds, and Snowflake-specific patterns like dynamic tables, zero-copy clones, warehouse routing, and clustering keys for cost control.

The course closes with a guided capstone — build the same Lumen Retail pipeline yourself, project structure through deployment — plus a portfolio-grade open challenge where you design (not just code) a complete architecture for a SaaS or Finance/CRM brief, graded on a real architect's rubric. Finish with a 50-point production checklist and exam-readiness coverage for the dbt Analytics Engineer certification.

By the end, you won't just know dbt syntax — you'll have the judgment to lead your team's dbt adoption, with a working reference project to prove it.

Who this course is for:

  • Data analysts who want to move from spreadsheets/stored procedures into modern analytics engineering.
  • Data engineers adding dbt + Snowflake to their toolkit, or standardizing a team onto this stack.
  • Anyone preparing for the dbt Analytics Engineer certification who wants hands-on practice, not just flashcards.
  • NOT for: complete programming beginners with zero SQL exposure (start with a SQL fundamentals course first).

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