How to Become a Data Engineer in 2025 💡 Want to break into data engineering or level up your skills for 2025? This video shares the shortest path to becoming a data engineer, whether you're starting from SQL/analytics or a software engineering background. 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. Stick around for actionable advice, real-world examples, and clear guidance. Timestamps 00:00 Intro: The shortest path to data engineering 00:22 Paths to Data Engineering: SQL/Analytics vs. Software Engineering 01:01 Timeless Skills: Focus on what lasts beyond tools and syntax 01:20 Analytics Path: SQL, DBT, warehouses, and business value 02:42 Software Engineering Path: Python, APIs, and project-driven learning 03:55 Extract (ETL): Python, APIs, and cloud storage 04:24 Load: Iceberg and the Lakehouse revolution 05:27 Transform: Spark vs. pandas and scaling data 06:25 Senior DE Skills: System design, database types, and trade-offs 06:54 AI Data Engineering: Tools like PyTorch, Ray, and dataset management 07:50 Dataset Management: Differences for analytics vs. AI pipelines 08:36 Project-Based Learning: The best way to master data engineering 10:58 Wrap-Up: Take your time, focus on fundamentals, and build real projects 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.