There are a few Bigcorps with real, novel Big Data datasets that need original statistical work slung at them. These companies need data scientists, for real: people with Ph.Ds in statistics. These people are indeed not interchangeable with programmers. They're partially interchangeable with academic computer scientists, and mathematicians, and physicists; but not with any trades-workers, including your average programmer. Their job is Science, capital-S: to do sound, replicable, high-powered experiments to learn things about the data. That is a skill in-and-of-itself, and one you need a lot of practice with—under the scrutiny of people like Journal editors—to get right (because it's hard to tell from your own observation if you've got it wrong.)
Everyone else, though—that is, all the companies but the SV Big Five and maybe some mobile-game and advertising firms—when they say they're looking for "data scientists", all they're really looking for is data engineers. The actual "data science" they're trying to do is a solved problem: you can find out the same thing a data scientist would tell you from five minutes of Googling the problem, because dozens of companies have solved the same problem for their own data-sets already and written case-studies on how they did it. You only need to implement said solution. Which, like I said, any competent programmer could do, after a few hours spent reading the API reference of a statistics package.
The majority of "data science" (read: data engineering) jobs aren't looking for people to Do Science; they're looking for regular programmers clever enough to have self-selected into the higher caste by doing nothing more than having learned some statistics-package APIs and then marketing that skill. (Much like the "officer class" of the American military in the 1800s allowed people to self-select into it just by being voracious readers.) It's basically an implicit open question at the beginning of the hiring process, "what do you want to be doing all day?", that is answered by choosing a different (pretend) job title.
What you describe with respect to data scientists needing help is what I'd call "data engineering." A data science person who is supposed to be doing production-facing engineering should be a competent enough data engineer that he or she can extract information from the data stores without having to have outside help most of the time, but strictly speaking, in my view, these are two different kinds of development.