So yeah, not for every problem (no framework is), but the problems that it solved it solved quite adeptly, and it was worth the cost (both initial and ongoing) of migration. It particularly proved its worth in the case where pipelines have grown in scale and complexity over time and thus gotten out of control. Particularly when there are thousands of lines of transforms -- that's when the ongoing maintenance burden gets so high that its completely worth the cost of migration. "Weeks going by and something seems off" we found often described the day to day of teams that manage codebases like that -- requiring them to spend tons of time working on the software engineering aspects of testing/visibility rather than developing new features to help the business.

Re: new data scientists, you'd be surprised at how adept they are at picking things up. There are a million frameworks/paradigms to learn when you join a company, so this isn't really an additional burden. And, once they learned that "one simple trick" (that parameter names map to the producing function), it ends up being pretty easy to use.