This article discusses a live session with Alejandro Aboy, a Senior Data and AI Engineer, about the evolving role of data engineers in the context of AI. Alejandro emphasizes that strong data engineering skills, particularly data modeling and orchestration, are crucial for successful AI work and shares practical steps for integrating AI into existing data workflows.

• Data modeling accounts for roughly 80% of the skill transfer from data engineering to AI engineering, according to Aboy.
• Good orchestration/workflow design can handle most AI tasks without needing complex agents.
• Precise column/model descriptions function as prompts for AI, so metadata and documentation quality directly determine AI performance.
• AI still lacks common-sense judgment (illustrated by a debugging agent missing context), so critically reviewing outputs rather than accepting them blindly remains essential.