SQL skills are expected of every data scientist and backend engineer, but are also found in a much larger and cheaper labor pool. As far as I can tell, the data engineering role exists to take advantage of those economics.

ETL pipeline queries and configs are absolutely critical, make or break an ML project, and account for most of its labor hours. But they are the most commoditized part of it.