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Joe Reis

Speaker

Joe Reis

3

talks

Joe Reis is a data professional with 20 years in the data industry, known as a "recovering data scientist" and a business-minded data nerd. His experience spans statistical modeling, forecasting, machine learning, data engineering, and data architecture. He is the co-author of Fundamentals of Data Engineering (O'Reilly, 2022).

Bio from: Small Data SF 2025

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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.

Face To Face
with Shachar Meir (Shachar Meir) , Guy Fighel (Hetz Ventures) , Rob Hulme , Sarah Levy (Euno) , Harry Gollop (Cognify Search) , Joe Reis (DeepLearning.AI)

Practicing analytics well takes more than just tools and tech. It requires data modeling practices that unify and empower all teams within analytics, from engineers to analysts. This is especially true as AI becomes a part of analytics. Without a governed data model that provides consistent data interpretation, AI tools are left to guess. Join panelists Joe Reis, Sarah Levy, Harry Gollop, Rob Hulme, Shachar Meir, and Guy Fighel, as they share battle-tested advice on overcoming conflicting definitions and accurately mapping business intent to data, reports and dashboards at scale. This panel is for data & analytics engineers seeking a clear framework to capture business logic across layers, and for data leaders focused on building a reliable foundation for Gen AI.

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.