USAWarez

Dbt + Snowflake: Complete Analytics Engineering Course
Published 7/2026
Created by Snowbrix Academy
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 121 Lectures ( 8h 15m ) | Size: 2.6 GB

Raw to production: layered models, testing, incremental loads, snapshots, CI/CD, and cost control on Snowflake.
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.
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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