These libraries leave most of the actual day to day work for ETL. ETL happens to be highly data and problem dependent, so it can't be easily automated or reused. For this reason I think the best thing to be a good applied ML person is a solid programming background. You should have a working knowledge of statistics and linear algebra, but the most useful skill really is being able to write good code. It's different for research of course.