Analytics Engineering with dbt Training Course

Course Introduction

Analytics Engineering with dbt Training Course provides a practical and structured learning path for professionals who want to transform raw data into reliable, well-modeled, documented, and analytics-ready datasets. The course focuses on the core principles of analytics engineering and shows how dbt can bring software engineering practices such as version control, testing, modular development, documentation, and deployment into modern data transformation workflows.

This comprehensive dbt training course develops the skills required to design maintainable transformation projects using SQL-based models, reusable logic, dependencies, configurations, and layered data structures. Participants learn how analytics engineers organize transformation pipelines, separate staging and business logic, establish consistent naming conventions, and create scalable analytical models that support reporting, dashboards, data science, and downstream business applications.

The course also explores dbt testing and data quality practices that help teams identify incorrect, incomplete, inconsistent, or unexpected data before it reaches business users. Participants learn how to design meaningful data tests, document models and columns, interpret test results, investigate failures, and establish repeatable quality controls. dbt's current platform documentation emphasizes building, documenting, testing, and collaborating on dbt projects as central parts of modern analytics engineering workflows.

Participants gain practical experience with project organization, source definitions, staging models, intermediate transformations, marts, snapshots, macros, packages, variables, seeds, and incremental models. The course connects these capabilities to real-world analytics engineering scenarios, helping learners understand how to build transformation projects that remain manageable as data volumes, business requirements, and development teams grow.

Advanced topics include dependency management, model selection, lineage, deployment workflows, performance optimization, incremental processing, reusable SQL logic, and environment management. The training also introduces current dbt capabilities such as dbt Catalog, dbt Orchestrator, dbt Semantic Layer, dbt Mesh, Studio IDE, and state-aware development concepts, giving participants an understanding of how modern dbt environments can support larger and more collaborative data teams.

By completing this Analytics Engineering with dbt Training Course, participants will be better prepared to contribute to modern data transformation initiatives and establish disciplined analytics engineering practices within their organizations. The hands-on approach emphasizes practical implementation, troubleshooting, maintainability, governance, and business-ready data products so learners can apply dbt techniques to real enterprise analytics environments.

Duration

5 days

Who Should Attend

• Data analysts who want to develop stronger SQL-based data transformation and modeling skills.

• Analytics engineers responsible for developing reliable analytical datasets and transformation pipelines.

• Data engineers who want to incorporate dbt into modern ELT and analytics engineering workflows.

• Business intelligence developers working with cloud data warehouses and analytical data models.

• BI professionals responsible for preparing trusted datasets for dashboards and reporting solutions.

• SQL developers seeking practical experience with modular transformation and analytics engineering practices.

• Data warehouse professionals involved in dimensional modeling, transformation, testing, and documentation.

• Data scientists who need dependable, well-structured datasets for analytical and machine learning workloads.

• Database professionals transitioning toward cloud-based analytics engineering and ELT architectures.

• Technical leads responsible for establishing data development standards and transformation best practices.

• Data quality professionals interested in implementing automated testing and validation within dbt projects.

• Data platform teams responsible for supporting scalable, collaborative, and governed analytics environments.

Course Objectives

• Develop a strong understanding of analytics engineering principles and how dbt supports reliable, maintainable, and collaborative data transformation workflows.

• Build structured dbt projects using modular SQL models, logical layers, dependencies, configurations, sources, and reusable transformation patterns.

• Design effective staging, intermediate, and mart models that transform raw warehouse data into consistent and business-ready analytical datasets.

• Implement dbt tests and data quality controls to identify unexpected values, broken relationships, missing records, and other transformation issues.

• Create comprehensive dbt documentation that improves data discovery, model understanding, lineage visibility, and collaboration between technical and business teams.

• Apply advanced dbt features including macros, Jinja templating, packages, variables, seeds, snapshots, and incremental models to solve practical transformation requirements.

• Manage model dependencies, selections, execution workflows, and deployment processes to improve reliability and efficiency across development and production environments.

• Apply analytics engineering standards for naming, organization, modularity, maintainability, code review, version control, and collaborative development.

• Analyze and improve dbt project performance by optimizing transformations, reducing unnecessary processing, and applying appropriate incremental and state-aware techniques.

• Understand modern dbt capabilities including Semantic Layer, Catalog, orchestration, Mesh, and collaborative development tools and evaluate their role in scalable data platforms.

Comprehensive Course Outline

Module 1: Analytics Engineering and dbt Fundamentals

• Understanding the role of analytics engineering and how it connects data engineering, analytics, business intelligence, and modern ELT architectures.

• Exploring dbt concepts, project structure, development workflows, environments, commands, configurations, and the overall lifecycle of a transformation project.

• Installing and configuring dbt development tools while understanding profiles, adapters, project settings, connection requirements, and warehouse integration.

• Examining the principles of modular SQL transformation, version-controlled analytics development, collaboration, documentation, and automated data quality practices.

Module 2: dbt Projects, Sources, and Data Modeling

• Creating well-organized dbt projects with appropriate directories, naming conventions, configuration files, model organization, and reusable development patterns.

• Defining source data and establishing clear relationships between raw warehouse tables, staging transformations, intermediate logic, and analytical models.

• Designing scalable data models using appropriate transformation layers while maintaining clear separation between technical preparation and business logic.

• Applying practical data modeling principles to create reliable analytical structures that support reporting, dashboards, business analysis, and downstream consumption.

Module 3: SQL Models and Transformation Workflows

• Building SQL-based dbt models that transform raw datasets into clean, standardized, reusable, and business-oriented analytical structures.

• Using model references and dependencies to create maintainable transformation graphs that allow dbt to understand execution order and project relationships.

• Applying reusable SQL patterns, transformations, joins, aggregations, filters, conditional logic, and dimensional modeling techniques within dbt projects.

• Developing modular transformation workflows that reduce duplication, improve readability, simplify maintenance, and support collaborative analytics development.

Module 4: Data Quality, Testing, and Validation

• Understanding the role of automated testing in analytics engineering and designing validation approaches that protect downstream users from unreliable data.

• Implementing common dbt tests for uniqueness, non-null values, accepted values, relationships, and business-specific data quality requirements.

• Investigating failed tests, tracing problems through model dependencies, and developing systematic approaches for diagnosing transformation and source-data issues.

• Establishing practical data quality standards that integrate testing, validation, documentation, monitoring, and continuous improvement into analytics workflows.

Module 5: Documentation, Lineage, and Data Discoverability

• Creating useful model and column documentation that communicates business definitions, transformation logic, assumptions, ownership, and intended analytical usage.

• Understanding dbt lineage and dependency graphs and using them to analyze upstream sources, downstream impacts, model relationships, and transformation dependencies.

• Developing documentation practices that improve collaboration between analytics engineers, analysts, data engineers, data scientists, and business stakeholders.

• Exploring dbt Catalog and related discovery capabilities for navigating projects and helping data users understand and leverage available dbt resources.

Module 6: Advanced dbt Development with Jinja and Macros

• Using Jinja templating to introduce dynamic SQL generation, reusable expressions, conditional logic, and parameterized transformation patterns into dbt projects.

• Creating and applying macros to reduce repeated SQL logic while improving consistency, maintainability, and development efficiency across multiple models.

• Working with variables, configurations, packages, and reusable project components to support flexible and standardized analytics engineering solutions.

• Applying advanced development techniques while maintaining readable, testable, documented, and maintainable transformation code across enterprise projects.

Module 7: Incremental Models, Snapshots, and Performance

• Designing incremental models that process appropriate subsets of data and reduce unnecessary transformation workloads in large-scale analytical environments.

• Understanding snapshots and applying appropriate historical tracking techniques when business requirements require changes to source records to be preserved.

• Identifying transformation bottlenecks and applying SQL optimization, model design, materialization, filtering, partitioning, and warehouse-specific performance techniques.

• Developing practical strategies for balancing transformation speed, data freshness, maintainability, warehouse resource consumption, and analytical requirements.

Module 8: Collaboration, Version Control, and Deployment

• Applying Git-based development practices to dbt projects, including branching, commits, pull requests, reviews, merge strategies, and collaborative change management.

• Establishing development, testing, and production workflows that allow analytics teams to introduce changes safely while maintaining reliable data products.

• Understanding CI/CD concepts for analytics engineering and integrating automated validation, testing, documentation, and deployment into controlled development workflows.

• Exploring modern dbt development environments and collaboration capabilities designed to support scalable analytics teams and enterprise data development practices.

Module 9: Orchestration, Semantic Layer, and Scalable Analytics

• Understanding how orchestration coordinates transformation workflows, dependencies, schedules, execution environments, and production data operations.

• Exploring dbt Orchestrator concepts and state-aware execution approaches for efficiently determining which models require rebuilding as projects and data change.

• Examining the dbt Semantic Layer and how centrally defined metrics can support consistent analytical definitions and reduce duplicated metric logic across data consumers.

• Understanding scalable analytics engineering concepts including project boundaries, shared data assets, reusable models, governance, and team-oriented development approaches.

Module 10: Enterprise dbt Practices and Capstone Implementation

• Designing an end-to-end analytics engineering project that integrates sources, staging models, transformations, tests, documentation, lineage, and production-oriented development practices.

• Applying project standards for maintainability, security, governance, performance, quality, documentation, naming conventions, and long-term operational support.

• Troubleshooting common dbt development challenges involving compilation, dependencies, SQL logic, source data, tests, configuration, execution, and deployment workflows.

• Reviewing the complete analytics engineering lifecycle and developing a practical roadmap for applying dbt effectively within modern enterprise data platforms.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.