dbt Analytics Engineering and Data Quality Testing Training Course

Course overview

Analytics teams often inherit SQL transformations that run without ownership, documentation or reliable checks. A dashboard can look credible while relying on duplicate customer records, late-arriving orders, broken joins or changed source values. This course equips analytics professionals to use dbt as an engineering framework for transforming warehouse data, defining trustworthy metrics and detecting data failures before they reach reports, operational teams or executive decisions.

Participants build a maintainable dbt project using layered models, source definitions, Jinja and macros, YAML documentation, version-controlled workflows and environment-aware configuration. They learn to write singular and generic tests, apply built-in tests for uniqueness, non-null values, accepted values and relationships, create custom data-quality assertions, investigate test failures, manage dependencies and run targeted jobs. The course also covers incremental models, snapshots, exposures, model selection syntax and approaches for making data-quality expectations visible to stakeholders.

Teaching combines instructor demonstrations with hands-on development in a realistic retail analytics warehouse. Each participant develops a dbt project that transforms raw order and customer data into documented analytical models, with tests, source freshness checks, lineage and a practical deployment plan. They leave with reusable SQL, YAML, macros and testing patterns that can be adapted to their organisation's warehouse and reporting estate.

The course is designed for intermediate SQL users who build, maintain or rely on analytical datasets. It is particularly valuable where teams need to replace fragile scheduled SQL scripts with controlled, testable transformations and establish clearer accountability for data quality.

Course objectives

By the end of this course, participants will be able to:

• Build a layered dbt project using staging, intermediate and mart model patterns
• Write modular SQL transformations with ref, source, Jinja variables and reusable macros
• Define source metadata, freshness thresholds and YAML model documentation
• Implement schema tests for uniqueness, non-null values, accepted values and referential relationships
• Create singular and custom generic tests for business-specific data-quality rules
• Configure incremental models and snapshots for efficient history-preserving transformations
• Use dbt selection syntax, dependency graphs and test outputs to isolate failed pipeline runs
• Produce a version-controlled dbt quality-testing plan for a production analytics domain

Benefits of attending

Target competencies

Who should attend

• Analytics Engineers — who need to build governed, testable transformation layers in the warehouse
• Data Analysts — who maintain SQL models and need reliable datasets for reporting and self-service analysis
• Data Engineers — who support warehouse pipelines and need consistent transformation testing practices
• BI Developers — who need documented, dependable marts behind dashboards and semantic reporting models
• Data Quality Analysts — who need to encode validation rules directly into analytical transformation workflows
• Data Platform Leads — who need to establish dbt standards, ownership and release controls across analytics teams

Requirements and prerequisites

Participants should be comfortable writing and reading SQL SELECT statements, joins, common table expressions, aggregations and basic window functions. They should understand the difference between source tables, transformed tables and analytical reports, and have some experience working with a cloud data warehouse or relational database. Familiarity with Git concepts such as repositories, commits and branches is useful, but not essential. No previous dbt experience is required, and participants do not need Python or data engineering experience. The course explains dbt project structure, YAML configuration and command-line workflows from first principles.

Training methodology

The instructor introduces each dbt capability through a short demonstration, then participants apply it in a working analytics repository connected to a training warehouse. Exercises progress from source modelling and SQL transformations to tests, documentation, snapshots and failure investigation. Small-group reviews examine how poor source data propagates into customer and revenue reporting, requiring participants to choose suitable controls. On the final day, participants complete an application-planning workshop that maps their dbt project structure, priority data tests, ownership model and first production use case.

Course outline

Day 1: dbt foundations and analytical model design

• Analytics engineering workflow and dbt project anatomy
• Installing and configuring dbt Core profiles
• Connecting dbt projects to a Snowflake target environment
• Staging, intermediate and mart layer design
• SQL model files and materialisation fundamentals
• ref and source functions for dependency management
• GitHub repository structure and collaborative branching practices

Workshop: Build a dbt project that converts raw customer and order tables into staged models and a first revenue mart.

Day 2: Reusable transformations and governed documentation

• Jinja templating in dbt SQL models
• Variables, environment configuration and target-aware logic
• Macros for reusable transformation logic
• packages.yml and dependency management
• YAML properties files for models and columns
• Source definitions, metadata and freshness configuration
• dbt docs generation and lineage graph interpretation

Workshop: Document the retail data domain, configure source freshness checks and create a reusable macro for standardised data cleansing.

Day 3: Data quality testing with dbt

• Data-quality dimensions for analytical datasets
• Built-in unique and not_null schema tests
• accepted_values and relationships test configuration
• Composite key and conditional test patterns
• Singular tests for SQL-based business assertions
• Custom generic tests with arguments and documentation
• Test severity, warnings and failure-store configuration

Workshop: Implement a test suite that detects duplicate customer keys, invalid order statuses, orphaned orders and negative revenue conditions.

Day 4: Reliable execution, history and failure analysis

• Incremental model strategies and unique key configuration
• Snapshots for slowly changing customer attributes
• Model materialisations: view, table, incremental and ephemeral
• dbt run, build, test and compile command behaviour
• Selection syntax for models, tags, paths and downstream dependencies
• Interpreting run results, logs and compiled SQL
• Exposures and downstream dashboard impact analysis

Workshop: Configure an incremental orders model and customer snapshot, then use targeted selectors to diagnose and remediate a simulated failed test run.

Day 5: Production-ready dbt quality practices

• Development, CI and production environment workflow
• GitHub pull requests and dbt code review criteria
• dbt Cloud job scheduling and run orchestration
• Deferral and slim CI concepts for efficient validation
• Data test ownership, triage and incident response
• Metric-critical model prioritisation and service expectations
• dbt adoption roadmap and project governance standards

Workshop: Produce a production-ready dbt implementation plan containing repository standards, priority models, test coverage, job schedule and failure ownership.

Tools & standards covered

dbt Core, dbt Cloud, Snowflake, GitHub

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions