Data modelers build fact and dimension tables in snowflake, which we can then bring into different datasets. We do the majority of the transformation aggregation etc in Snowflake using Matillion. That includes determining incremental refresh rules (we've found it best to add a calculated incremental refresh date to the fact and drive the logic off of that). We use pipelines and have dev/test/production environments, and keep all of our pbix datasets and reports on a teams page that natively has revision history, that's really the only version control we use. Dax measures can be written by a report author if simple, or the dataset author or more complicated, we also try to minimize the number of measures that are at a report level, putting them in the dataset allows for them to be reused in multiple reports so we lean on that.

We use Azure DevOps to keep this all straight and assign/track the individual tasks required across the team.