"For Scientists and Engineers" sounds to me like it's targeting people who already have a strong background in more advanced mathematics, but not necessarily probability and measure theory. If so I think this is a decent way to go about it.

I'm an engineer and sometimes mathematician that works with fairly in-depth probability theory related things and this looks to be a condensed version of a lot of the basic stuff I had to self learn when I was getting into what I work on now. I'm in a niche area though, and I do wonder if this really is that useful to most scientists and engineers.

Reading 'All of statistics' by Wasserman would be a much better bet than diving deep into measure-theoretic probability though.

Depends on your goal, statistics and probability theory are separate (though of course related) fields with different applications. For me I really needed the measure-theoretic bits because I was (am) working on modeling ergodic processes. This article honestly doesn't go into enough detail to be especially useful but I like the direction the author approaches it from.

I'm familiar with the text you mentioned, it's certainly good and would be better than this article for most, but comparing a textbook to a short web article isn't exactly fair.