Pipeline Engineer: Building Modern Data Infrastructure with Python, Airflow, dbt, and the Cloud

By RICHARD BOOZMAN

Design scalable, reliable, and production-ready data platforms for modern analytics and machine learning

Data systems are the backbone of modern organizations.

From analytics dashboards and business intelligence to machine learning pipelines and real-time decision systems, companies depend on reliable data infrastructure to operate effectively.

"Pipeline Engineer" is a practical, engineering-focused guide to building modern data platforms using Python, Apache Airflow, dbt, and cloud-native infrastructure.

This book teaches developers and data engineers how to design, orchestrate, transform, monitor, and scale production-grade data systems.

Why modern data engineering matters

Organizations today face challenges such as:

fragmented data sourcesunreliable pipelines and failed jobspoor data quality and governancescaling transformation workloadsoperational complexity across cloud systemsmaintaining observability and lineage

Building dependable data infrastructure requires both software engineering discipline and operational reliability.

What you will learnfundamentals of modern data architecturedesigning ETL and ELT workflowsworkflow orchestration with Airflowtransformation modeling with dbtscalable data ingestion patternsdata warehouse and lakehouse conceptspipeline testing and validationobservability and monitoring strategiescloud-native deployment workflowssecurity, governance, and access management
From raw data to reliable platforms

Throughout the book, you will learn how to:

design maintainable data pipelinesorchestrate complex workflow dependenciesbuild reusable transformation layersimprove data quality and reliabilitymonitor pipelines proactivelyscale data infrastructure across cloud environmentsmanage production operations confidently

Each chapter focuses on practical workflows used in real-world data engineering teams.

Practical applicationsanalytics engineering platformsbusiness intelligence pipelinesmachine learning data infrastructureevent-driven data systemscloud-native ETL and ELT platformsenterprise reporting and governance systems

These examples reflect real production data engineering challenges.

Who this book is fordata engineersanalytics engineersbackend developerscloud engineersmachine learning infrastructure teamssoftware engineers transitioning into data platforms

If you want to build scalable, maintainable, and production-ready data systems, this book provides the roadmap.

Move data reliably.
Transform intelligently.
Engineer infrastructure that scales.

• Format: Paperback
• Language: English
• ISBN: B0H49YMYK8
• ISBN13: 9798180305015
• Release Date: June 2026
• Publisher: Independently Published
• Length: 288 Pages
• Weight: 0.77 lbs.
• Dimensions: 0.7" x 6.0" x 9.0"