dbt & Analytics Engineering

Build modular data models, Jinja macros, testing, and CI/CD deployment.

dbt is how mature analytics orgs ship SQL: modular models, automated tests, version control, lineage, and CI/CD. JetBlue, GitLab, Hubspot, Convoy, and thousands of other companies treat it as the standard transformation tool. Senior analytics engineers are expected to defend their model layering, incremental strategy, test design, and semantic-layer / dbt-mesh choices.

What you’ll be able to do

Build modular dbt models with staging, intermediate, and mart layers
Write comprehensive tests and data quality checks
Master Jinja macros and packages for reusable logic
Deploy dbt to production with CI/CD and dbt Cloud

Curriculum

Phase 1: Build Your First Analytics Models

Fundamentals, layered modeling, and testing — go from a single SELECT to a tested staging/intermediate/mart project in one phase.

dbt Fundamentals

dbt Core setup, project structure, sources + refs, your first model, the schema.yml contract, and the basic schema tests that turn ad-hoc SQL into version-controlled analytics.

Analytics Modeling with dbt

The staging / intermediate / mart layering pattern, dimensional modeling (Kimball facts + dims), star schemas in dbt, surrogate keys with dbt-utils, and the directory conventions that scale to 200+ models.

Core Data Quality

Schema vs data tests, the four built-in generic tests, custom singular tests, the dbt-expectations package, and a testing philosophy that catches issues before stakeholders open the dashboard.

Phase 2: Reuse & Scale Your dbt Project

Jinja and incremental models — the code-reuse + performance layer that turns a working dbt project into a maintainable one your team can grow into.

Reusable & Scalable dbt

Jinja fundamentals, writing custom macros, the dbt-utils + dbt-expectations + audit-helper packages, advanced templating patterns, and macro testing — the layer that turns 200 ad-hoc models into a maintainable library.

Performance & Incremental Models

Incremental models deep dive, merge / append / delete+insert strategies, watermarks, partitioning + clustering decisions, query optimization, and the cost-reduction patterns that decide whether your dbt run is 6 minutes or 60.

Phase 3: Production dbt & the Semantic Layer

CI/CD deployment plus the semantic + governance layer — Slim CI, dbt Cloud vs Core, MetricFlow, exposures, dbt Mesh, and the multi-project governance patterns mature analytics orgs run on.

dbt in Production

CI/CD with GitHub Actions, Slim CI (state:modified+), environment management (dev / staging / prod), dbt Cloud vs Core tradeoffs, Airflow orchestration integration, and the production checklist for a launch review.

Semantic Layer & Advanced dbt

The dbt Semantic Layer + MetricFlow, exposures + downstream lineage, advanced observability, dbt Mesh + cross-project refs, governance patterns, and a capstone that ships everything end-to-end.

What you’ll build

Layered dbt project (staging / intermediate / mart) with 30+ schema tests and dbt-utils surrogate keys
Incremental model with merge strategy + watermark that runs in minutes, not hours
Jinja-macro library + custom schema tests using dbt-utils + dbt-expectations packages
MetricFlow semantic layer + exposures + Slim CI on GitHub Actions, with dbt-mesh-ready project structure

Your dbt project worked great with 20 models… and now at 200 models it runs for two hours and breaks silently.

Without production-grade dbt, you risk:

Silent metric drift because nobody owns test coverage and a column rename ships unnoticed
Two-hour dbt runs because incremental strategy + watermarks were never tuned for real data volume
Breaking changes that hit downstream consumers because dbt contracts and exposures weren't wired
Fragmented metric definitions across BI tools because the semantic layer + MetricFlow rollout never happened

What is dbt & Analytics Engineering?

dbt (data build tool) is an open-source transformation framework that lets data teams write modular SQL models with built-in testing, documentation, and version control. dbt has become the industry standard for analytics engineering, used by thousands of companies including JetBlue, Hubspot, and GitLab to transform raw data into reliable analytics.

Why this matters in production

Production data teams use dbt to manage hundreds of SQL models with proper testing and CI/CD deployment. At companies like Gitlab, dbt runs thousands of models daily with automated quality checks. Without dbt, SQL transformations become unmaintainable spaghetti that breaks silently.

Common use cases

Building staging, intermediate, and mart model layers for analytics warehouses
Writing automated data quality tests that catch issues before stakeholders do
Creating reusable Jinja macros for common transformation patterns
Deploying SQL transformations with CI/CD using dbt Cloud or GitHub Actions
Generating documentation and lineage graphs for data governance
Implementing semantic layers for consistent metric definitions

DBT vs alternatives

DBT vs Stored Procedures

dbt provides version control, testing, and documentation that stored procedures lack. dbt models are SQL SELECT statements managed like software, while stored procedures are database-specific and hard to test.

DBT vs Dataform

dbt has a larger community, more packages, and broader warehouse support. Dataform is Google-owned and tightly integrated with BigQuery. Most teams outside the Google ecosystem choose dbt.

DBT vs Custom Python ETL

dbt handles SQL transformations with built-in testing and lineage. Custom Python ETL is needed for non-SQL logic, API calls, and orchestration. Most teams use dbt for transformations and Python for everything else.

Related skills

SQL Mastery — dbt models are written in SQL, so strong SQL skills from
Data Modeling — dbt implements the dimensional models designed in
Data Observability — dbt tests are a key part of the observability practices in

Why this skill matters

dbt is the most-requested analytics-engineering skill in DE / AE job listings. Senior + Staff analytics-engineering roles at data-mature orgs (JetBlue, GitLab, Hubspot, Convoy, Reddit) hire specifically for engineers who can defend incremental strategy, test design, Jinja-macro architecture, and semantic-layer / dbt-mesh decisions — the exact tradeoffs this path makes you defensible on.

Common questions about DBT

What is dbt used for?

dbt transforms raw data into analytics-ready models inside your data warehouse. It manages SQL transformations with testing, documentation, and version control — the standard workflow for analytics engineering.

Is dbt still relevant in 2026?

dbt is the dominant transformation tool in modern data stacks. With dbt Mesh, semantic layers, and growing LLM integrations, its relevance continues to increase.

How long does it take to learn dbt?

Basic dbt takes 1-2 weeks if you know SQL. Production-level dbt with Jinja macros, custom tests, and CI/CD deployment typically takes 4-6 weeks of practice.

Do data engineers need dbt?

Yes. dbt is expected knowledge for both data engineers and analytics engineers. It appears in most job descriptions and is the standard way teams manage SQL transformations.

dbt Core vs dbt Cloud?

dbt Core is the free open-source CLI. dbt Cloud adds a web IDE, job scheduling, and managed infrastructure. Most teams start with Core and move to Cloud as they scale.

Can dbt replace a data pipeline?

dbt handles the Transform step but not Extract or Load. You still need ingestion tools like Fivetran and orchestrators like Airflow. dbt is one critical piece of the data stack.

dbt Mesh vs a single dbt project — when should I split?

Stay single-project while your org has a small number of analytics-engineering owners and your model graph is reviewable by one team. Adopt dbt Mesh once multiple teams own disjoint subsets of the warehouse, model PRs start blocking each other, or you need to expose certified data products across team boundaries with versioned contracts. The trigger is organizational scale (multiple owning teams) more than model count.