dbt (data build tool) — Analytics Engineering
A two-day dbt course built around the full analytics engineer role — data testing, documentation, CI/CD, and the semantic layer — not just data transformation on its own. dbt has become the modern data stack standard; the course prepares you for the practice of the role, not a feature list of the tool.
dbt as a role change, not just a tool
Analytics engineering is not a new name for a data analyst’s old tasks — it is a combination of engineering discipline (code in Git, testing, code review, CI/CD) and an analyst’s domain knowledge. The course presents dbt as the tool that enables this role change, not as an isolated product to learn feature by feature.
Testing and documentation as a standard, not an add-on
Data models without tests and documentation quickly lose the team’s trust. The workshop treats data testing (built-in and custom) and documentation as an integral part of every dbt model, building a habit that carries over to the organization’s entire data pipeline — not just the training project.
The semantic layer ends the metric-definition war
The final module shows how the dbt Semantic Layer and MetricFlow solve the age-old problem of diverging business metric definitions across teams, and how dbt Mesh lets you scale that consistency across multiple domain data teams in a large organization.
Benefits
- Build data models in dbt following analytics engineering practices
- Design data testing and documentation as an integral part of the pipeline
- Implement CI/CD for dbt projects — automated testing and rolling out changes
- Design a semantic layer (dbt Semantic Layer, MetricFlow) for consistent business metrics
Who is this training for?
Prerequisites
- Intermediate-level SQL knowledge
- Basic familiarity with Git and working with code repositories
Training program
Analytics engineering as a role, not a bundle of SQL scripts
- The evolution of the role: from data analyst, through data engineer, to analytics engineer
- Data modeling in dbt: staging, intermediate, marts — project structure
- Materializations: table, view, incremental, ephemeral — when to use which
- Jinja and macros as a mechanism for reusing transformation logic
Data testing and documentation as a team standard
- Built-in tests (unique, not_null, relationships) and custom tests
- Model documentation as part of the code, not a separate artifact
- Data contracts — formalizing expectations about data structure between teams
- Monitoring data quality and alerting on test failures
CI/CD for dbt projects
- Automatically running tests on every pull request
- Slim CI — testing only changed models for faster feedback
- Rolling out changes to production: dbt Cloud vs. dbt Core in a CI/CD pipeline
- Integrating with Snowflake, BigQuery, Redshift, and Databricks
Semantic layer and scaling to multiple teams
- dbt Semantic Layer and MetricFlow — one metric definition for the whole organization
- dbt Mesh — splitting a large dbt project across domain teams
- Data governance in a distributed analytics engineering team
- The analytics engineer career path
Delivery Methods
Online
- Convenience of participating from anywhere
- Interactive live sessions with trainer
- Materials available for 30 days
- No travel costs
On-site
- Direct contact with trainer and group
- Intensive hands-on workshops
- Networking with other participants
- Full focus on learning
Frequently asked questions
How is this different from dbt — Data Transformation in Practice and Introduction to dbt Cloud in our catalog?
Both existing courses focus on the tool's features: modeling, testing, documentation, CI/CD (transformation in practice) or an introduction to the dbt Cloud environment. This course approaches dbt from the analytics engineer role perspective — showing how dbt changes the way an entire data team works, with an emphasis on the semantic layer and dbt Mesh for scaling to multiple teams, which the other two courses do not cover.
How is an analytics engineer different from a classic data analyst?
An analytics engineer combines engineering skills (version control, testing, CI/CD) with a data analyst's domain knowledge — instead of writing one-off SQL queries, they build versioned, tested, and documented data models that become a shared source of truth for the entire organization.
What is a semantic layer and why does it matter?
A semantic layer (the dbt Semantic Layer, built on MetricFlow) defines business metrics once, in one place, and serves them consistently to different BI tools — eliminating the situation where two teams calculate "revenue" differently. The course shows how to design a semantic layer for an organization with multiple analytics teams.
Is dbt Core enough, or do I need dbt Cloud?
It depends on team scale and requirements for a graphical interface, scheduling, and built-in CI. The course covers both approaches — dbt Core as an open-source tool run in any CI/CD pipeline, and dbt Cloud as a managed environment with additional features — with selection criteria for different organizational contexts.
Request a quote
Funding Options
Check funding options for your company
Development Services Database
Up to 80% funding for SMEs from EU funds
Check availabilityNational Training Fund
Up to 100% funding for employers
Learn moreTrusted by
We train teams at Poland's largest companies
Interested in this training?
Contact us - we'll prepare an offer tailored to your organization's needs.