Data Engineering Roadmap

Data engineering is the practice of building and maintaining the infrastructure that collects, processes, and stores data for analysis. While data scientists build models and analysts build dashboards, data engineers build the pipelines that move terabytes of data daily, transform raw events into analytics-ready datasets, and maintain the systems that power data-driven decisions. This roadmap teaches you the full data engineering stack—from moving data between systems to building analytical models in a data warehouse.

You’ll learn how to design data pipelines that are reliable and maintainable, choose between batch and stream processing, build data warehouses that enable fast queries at scale, and implement data quality checks that catch problems before they reach analysts. Whether you’re at a startup building your first analytics infrastructure or an enterprise modernization legacy ETL, these skills are essential.

Before You Start

• Basic SQL proficiency (SELECT, JOIN, GROUP BY)
• Familiarity with at least one programming language (Python preferred)
• Understanding of basic data structures (tables, rows, columns)
• Basic knowledge of databases and SQL
• Understanding of how applications produce and consume data

The Roadmap

Resources

Books

• Data Engineering with Python and AWS Lambda by Louis-Etienne Dorn
• The Data Warehouse Toolkit by Ralph Kimball
• Fluent Python by Luciano Ramalho

Official Documentation

• Apache Kafka Documentation
• Apache Spark Documentation
• Apache Airflow Documentation
• dbt Documentation

Reference Architecture

• AWS Big Data Blog
• GCP Data Analytics Blog
• Patterns of Distributed Systems