Analytics Engineer: dbt, SQL Modeling, and Modern Data Transformation for Business Intelligence Teams

By: Boozman, Richard

Build trusted, scalable, and maintainable analytics systems for modern data organizations

Raw data is rarely useful on its own.

Before organizations can make decisions, data must be cleaned, modeled, transformed, documented, tested, and structured into reliable analytical systems.

That is the role of the modern analytics engineer.

"Analytics Engineer" is a practical, engineering-focused guide to building production-grade analytics workflows using dbt, SQL modeling techniques, and modern data platform practices.

This book teaches data professionals how to transform fragmented raw data into trustworthy business intelligence systems that scale across teams and organizations.

Why analytics engineering matters

Modern data teams face challenges such as:

inconsistent business metricsduplicated transformation logicunreliable dashboards and reportingpoorly documented datasetsdifficult-to-maintain SQL pipelinesweak governance and testing practices

Analytics engineering bridges the gap between data engineering and business intelligence by bringing software engineering discipline into analytics workflows.

What you will learnfundamentals of analytics engineeringmodern ELT and transformation workflowsbuilding modular SQL models with dbtdimensional modeling and warehouse designtesting and validating analytical datadocumentation and lineage trackingreusable business metrics and semantic layersversion control and collaborative analytics workflowsorchestrating transformations in productionoptimizing query performance and warehouse costs
From raw warehouse tables to trusted metrics

Throughout the book, you will learn how to:

structure maintainable transformation layersstandardize business definitions across teamsimprove reliability and trust in analytics systemsbuild scalable SQL modeling workflowsreduce technical debt in reporting pipelinesmanage analytics projects like production software

Each chapter focuses on practical workflows used in modern analytics and data platform teams.

Practical applicationsbusiness intelligence systemsanalytics transformation pipelinesKPI and reporting platformscustomer and product analyticsfinance and operations reporting systemsenterprise data warehouse workflows

These examples reflect real-world analytical engineering challenges.

Who this book is foranalytics engineersdata analysts transitioning into engineeringdata engineersBI developersSQL developersteams building modern data platforms

If you want to build analytics systems that are trusted, maintainable, and scalable, this book provides the roadmap.

Model clearly.
Transform reliably.
Engineer analytics that teams can trust.

• Format: Paperback
• Language: English
• ISBN: B0H4B3GKBG
• ISBN13: 9798180309761
• Release Date: June 2026
• Publisher: Independently Published
• Length: 294 Pages
• Weight: 0.79 lbs.
• Dimensions: 0.7" x 6.0" x 9.0"