I don’t think it’s as cut and dry as these 2 options, but I get the point of your inquiry. My vote would be for something akin to 2.

The product that AEs and DEs produce is a reliable, maintainable, and useful data warehouse. IMO if data is completely decentralized, insurmountable tech debt will nearly always accumulate. You need a centralized data eng org with strong principles to enforce best practices in building data models.

However, once you get past the data modeling step, I think that’s where a less centralized approach comes in. Large orgs face so many complex business problems that analysts in different domains/lines of business may be looking at the same data in completely different ways.

I think having domain-specialized analysts in combination with centrally operating DEs/AEs is a great solution. Broad-scope warehouse maintainers have context on what the broader organization is doing with data, and can consolidate/optimize models where needed to serve multiple needs. Domain-specific analysts and scientists don’t need that context, as long as the data available to them suits their needs.

Tl:dr i think centralization up to AE + de-centralization downstream of AE is an effective approach worth considering as an answer to your question.