Build the Perfect Data Stack for Analytics Engineering

DBT Production Setup : Data Modeling, Automation, CI/CD & Cost Optimization

What you'll learn
- Build a production-ready DBT project and understanding everything about DBT set up
- Having best practices about data modeling and SQL code convention
- How to automate everything that is too time consuming : testing, documentation and cleaning
- Monitor and optimize data warehouse costs

Requirements
- Know how to code in SQL
- Maybe an idea of what DBT is

Description
Master the complete analytics engineering workflow by building a production-ready data stack from scratch using DBT (Data Build Tool), the industry-standard transformation framework trusted by data teams worldwide.

This comprehensive course takes you from zero to advanced DBT practitioner, covering everything needed to build, deploy, and maintain scalable data pipelines in real-world production environments. You'll learn the exact methodologies and best practices I've developed over 12+ years working across data analyst, data scientist, and analytics engineer roles in fast-growing startups.

What you'll build:

Complete three-layer data architecture (staging, intermediate, mart) following software engineering principles

Automated CI/CD pipelines with DBT Cloud for pull request testing and production deployments

Cost monitoring system to track and optimize data warehouse expenses

Self-healing testing framework with automated failure remediation

Production-grade incremental models for efficient data processing

Key topics covered:

DBT project setup with development/production environment separation

Granularity-based data modeling that scales from thousands to billions of rows

Version control workflows with Git and automated quality enforcement via pre-commit hooks

SQL linting with SQLFluff and automated documentation generation

Workflow automation using Makefiles and GitHub Actions

Query cost attribution and optimization strategies

Advanced DBT features: seeds, macros, snapshots, and custom tests

Who this is for:Data analysts transitioning to analytics engineering, data engineers building transformation layers, or anyone responsible for maintaining data pipelines serving hundreds of employees and millions of rows.

By the end, you'll have a battle-tested, production-ready data stack that actually works at scale—not just theory, but proven practices from real company environments.

Who this course is for:
- anyone interested in analytics engineering or in data platform construction
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