AI is rapidly automating much of the traditional work performed by data engineers.
Pipelines are built faster, code is generated automatically, and operational tasks are increasingly moving into the background. Does this mean the need for data engineering is declining?

A small spoiler: we believe the exact opposite is true.

As AI automates execution, the demand for reliable data foundations, semantic models, governance, and business context continues to grow. Organizations are discovering that the biggest obstacle to successful AI adoption is not the models themselves, but the quality, accessibility, and trustworthiness of their data.
In this session, we explore how data engineering is evolving from building pipelines to designing the enterprise memory: a platform where data is made understandable, trustworthy, and usable for both humans and AI systems. We will touch upon the concept of AI-ready data and provide practical guidance on how organizations can start their journey with a single use case and scale from there. With example of an use case for conversational analytics on your semantic model.
AI is changing how we build data platforms. This session explores what that means for the future role of data engineering.