Cloud costs increasing could be a sign of more data being utilized productively. In any way, a mixture of data engineers and analysts is a healthy way to scale a dbt project to increase speed-of-delivery of analytics requests vs cloud costs. We at dbt Labs encourage the "analytics engineering" mindset to bring software engineering best practices into the dbt analytics workflow (git version control, code reviews), and so cloud cost considerations should be incorporated into mature dbt development practices.
Check out dbt-expectations package[1]. It's a port of the Great Expectations checks to dbt as tests. The advantage of this is you don't need another tool for these pretty standard tests, and can be early incorporated into dbt workflows.