One of the better ways I've seen dbt described is very similar to an MVC framework for data (I didn't see that mentioned yet, if it has been sorry I missed it). Where a rough mapping would be:

model >> source

controller >> ephemeral model

view >> materialized (view, table, incremental) model

Like many MVCs it provides abstractions and helpers for common tasks such as performing incremental updates, snapshotting time series, common manipulations like creating date spines. Like many MVCs it supports a robust ecosystem of plugins that make it easy to re-use stable transforms for common datasets. Where Django passes you the request object instead of hand-parsing http responses, dbt allows you to loop through a set of derived schemas and dry out your SQL code.

You would generally use dbt as a transform framework for the same reason you'd use Ruby on Rails or Django etc as a web framework - because it provides you with a ton of otherwise repetitive non-differentiating code pre-baked and ready to go. You could keep a folder of sql files you arbitrarily run, and you could roll your own web framework from scratch. Personally I wouldn't do those things.