A paradigm shift is underway; the primary consumer of data is evolving from human analysts to AI agents. This presents a strategic challenge to every data leader: how do we architect an ecosystem that satisfies relentless, machine-scale demand for governed data without overwhelming our most valuable human experts? A chaotic free-for-all, with AI agents querying sources directly, is a regression that would erase a decade of progress in data warehousing and governance.

To solve this machine-scale problem, we must deploy a machine-scale solution. This session casts a vision for the future, exploring why current models are ill-equipped for the AI era. We will introduce the concept of the virtual data engineer—an AI-powered partner designed to augment and accelerate human capabilities on a collaborative platform. Discover how to evolve your team and architecture to turn this challenge into a strategic advantage, ensuring you lead the way through this transformation.

This session will explore the evolving role of data engineers. Data engineering is currently a bottleneck due to overwhelming requests and complex knowledge work. Maia acts as a "digital data engineer" or a "virtual data team" that amplifies productivity by 100x. It enables users, from skilled engineers to citizen data analysts, to author pipelines in natural business language. The session will demonstrate Maia's ability to accelerate mundane and advanced tasks,troubleshoot and debug pipelines in real-time, and generate high-quality, auditable pipelines using Matillion's proprietary, human-readable Data Pipeline Language (DPL), which avoids "spaghetti code" common with generic LLMs.