As those demands grew, so did the pressure on the data team, including more pipelines, more use cases and more coordination across teams. To address this, the team defined a more structured operating model for how pipelines are designed, built and governed, and used Databricks Genie Code to accelerate execution within that model.

The team leveraged Genie Code as an AI-assisted approach to data engineering. Working directly against governed data in Unity Catalog can help plan and generate multi-step pipelines across the workflow. This allows engineers to move from an idea to a working pipeline much faster, without switching tools or manually stitching components together.

That speed fundamentally changed how the team worked. Pipelines that previously took days to prototype could be generated in minutes. Iteration cycles were shortened, and engineers spent less time writing boilerplate and more time refining logic and outcomes.

At the same time, operating in a large, shared data environment requires consistency. Pipelines must follow common architectural patterns, use shared definitions and behave predictably across teams.