The long-term impact of AI on data engineering jobs is a story of transformation—both in the skill sets required and the nature of the work itself. Data engineers will need to embrace AI as both a challenge and an enabler: acquiring new AI-related skills to meet the growing demands of the industry while leveraging AI tools to boost productivity and enhance creativity. The future will favor engineers who adapt to these changes, blending traditional data engineering expertise with an evolving AI-first approach. As AI adoption is expected to be personal rather than a broad process change, it's recommended that executives encourage experimentation and sharing of best practices on how AI can boost productivity, allowing team members to figure out their adoption journey. Additionally, AI tools will empower data engineers by fundamentally changing how we work, with coding assistants providing the most value during an engineer's ramp-up phase, while introducing Gen AI into ideation sessions has been shown to stifle innovation in some studies. Ultimately, the advent of Gen AI will only ramp up expectations of what the data engineering function can deliver, requiring a sizable investment from executives and engineers alike.