How I'd Learn Data Engineering in 2025

(from a Staff AI Data Engineer)

How to Become a Data Engineer in 2025

This video shares the shortest path to becoming useful enough to get a job as a data engineer. I'll also share how I’m going deeper into AI data engineering myself.

You don’t need every tool or an overwhelming roadmap. Instead, I’ll help you:

• Leverage your strengths to break into the field.

• Focus on timeless skills that AI can’t replace.

• Build real-world projects that make you job-ready.

📌 Whether you’re just starting or looking to specialize further, this video cuts to the core of what matters in data engineering.

Key Takeaways

• SQL/Analytics Path: Master SQL, DBT, and warehouses while focusing on data exploration and business value.

• Software Engineering Path: Go deep on Python, APIs, and Spark while learning how to handle real-world problems.

• Timeless Skills: Tools and syntax change, but foundational knowledge like database design and ETL pipelines lasts.

• AI Data Engineering: Dive deeper into dataset management, distributed tools, and preparing data for AI research teams.

• Project-Based Learning: Build something real—use APIs or datasets that interest you, and learn by solving problems.