[Dwolb]

Love the post and I don’t think enough businesses today see data as core to their ability to execute well.

First question, how do you see definitions getting managed between the client team and data team?

eg “this is the canonical definition of churn”

Second question, where do you think custom and 3rd party infrastructure management sits (especially in the case where the data team doesn’t sit under the engineering org)?

[claytonjy]

To your first question, an overly general answer is "client and data team collaborate to find the canonical definition, document it in one place comfortable to both teams, and both teams know how to update or iterate that definition when needed". Probably looks like analytics engineers trying a handful of SQL until the other team agrees with the results, focusing on the tricky/edge cases and not drowning the client team in a big CSV. Then formalize as a dbt model.

Second question, I think platform teams make sense, and at a large enough org this could be multiple layers with "data platform" sitting on top of "cloud platform". It's much less clear to me how to allocate those responsibilities when all the folks involved fit in a team or two instead of 3+. It's also unclear to me if something like Kafka is more "cloud platform" or "data platform".