Data Mesh promises scalability and autonomy, but orchestration often becomes the hidden bottleneck: as data products grow, cross-domain dependencies explode, DAGs become fragile, and upstream teams are forced to react to every new consumer.

In this talk, we present a platform-designed orchestration solution built on top of Dagster that enables company-wide orchestration without coupling data products together.

Instead of requiring data engineers to manually define DAGs or modify upstream pipelines, each data product simply declares its dependencies on upstream output ports. From these declarations, the platform automatically builds and maintains the global DAG behind the scenes, ensuring:

- upstream data products remain completely unaffected by new consumers
- orchestration scales as the number of data products grows
- platform standards are enforced without slowing down teams

We’ll walk through:

- the orchestration challenges we faced when adopting data mesh at scale
- how Dagster was extended and shaped into a platform abstraction, not exposed directly to product teams
- the Python-based design patterns used to dynamically generate and evolve DAGs
- the resulting developer experience for data engineers building data products

This session is a practical, production-tested story of how Python and Dagster can be used to enable scalable orchestration in a real data mesh implementation — without turning the platform team into a bottleneck.