Founder of an ETL startup here. This is exactly what we believe: the end-user of the data should be involved as early in the data pipeline as possible, including the wrangling. If you eliminate the engineer from the ETL process you remove a lot of painful back-and-forth and get more flexible pipelines.
But in many cases it's a technical debt issue. In the early startup stages when you're struggling for product-market fit, cleaning or auditing your data is not a good use of time. When a business like that starts to get traction, it finds itself with clients that now want more reliability, scaling issues, and a bunch of data that no-one really remembers where it all came from or how it was generated. A lot of the big-data startups are hitting that stage now.