In data engineering, the tools never stop changing. From on-premises ETL to cloud-native pipelines, from dashboards to AI-driven insights, the landscape evolves faster than most teams can keep up. But the data engineers who thrive aren’t the ones who know a single tool inside out; they’re the ones who adapt, learn, and apply new practices as the field transforms.

In this session, we’ll explore why adaptability is the most critical skill in the AI era. You’ll learn how to evaluate new technologies, when to embrace the latest innovations (like generative AI for pipeline automation), and when to stick with proven practices. Real-world stories from 25 years in the field will highlight how continuous learning turned potential failures into successful, future-ready data projects, and has kept me in a career that I love.

Agenda (60 minutes)

The only constant: change in data engineering (5 min).

A short evolution tour (10 min): from DTS → SSIS → ADF → Databricks & Fabric.

How AI changes the stakes (15 min): automation, copilots, and the importance of the "human in the loop"

Frameworks for adaptability (15 min): evaluating trends vs. hype, choosing what to learn.

Habits for continued growth (10 min): sustainable learning routines that fit busy engineers.

Q&A and audience stories (5–10 min).

Key Takeaways

Adaptability: not a single tool, is the most valuable long-term skill.

How AI raises the bar for learning speed and breadth in data engineering. (Evolution from Stack Overflow to Chat GPT)

A practical framework for evaluating new tools without getting caught in shiny-object syndrome.

Habits and resources that make ongoing learning realistic and effective.