This session will go over 4 common data engineering problems, myth busting the "ask" vs "reality" with real-life examples. Part data engineering therapy, part solving problems that "really" matter, part how to think small and deliver big.

To be clear - I'm not saying that analytics and data engineering are a fad. I'm not saying the data teams are doomed to fade away, or that the old fundamentals of data modeling are wrong, or that the urge to quantify everything is a mistake. I'm saying that things seem pretty good, right now. But, you know. Like Charles Schwab constantly says, past performance is no guarantee of future results. So someone else might say all of that in the future - because, as John Maynard Keynes said, in the long run, we are all dead.

For years, data engineering was a story of predictable "pipelines": move data from point A to point B. But AI just hit the reset button on our entire field. Now, we're all staring into the void, wondering what's next. While the fundamentals haven't changed, data remains challenging in the traditional areas of data governance, data management, and data modeling, which still present challenges. Everything else is up for grabs. This talk will cut through the noise and explore the future of data engineering in an AI-driven world. We'll examine how team structures will evolve, why agentic workflows and real-time systems are becoming non-negotiable, and how our focus must shift from building dashboards and analytics to architecting for automated action. The reset button has been pushed. It's time for us to invent the future of our industry.