I believe that the comment you are responding to was speaking to the asymmetry that people with experience in computational biology have an easier time moving to general data science problems than do people with experience in general data science working on computational biology problems.
I agree that the asymmetry exists: there is a tremendous baseline of scientific knowledge and experience that is needed to make significant contributions to the field. I personally have worked with people with backgrounds in programming or CS on medical problems, and it has been frustrating because they lack what I would term "scientific common sense". I would personally prefer, and would be able to make more progress with, working with (for example) anyone who has completed a sequence of education sufficient for pre-med requirements and has some programming experience over a "full stack data engineer". Even if someone with a programming or CS background were inclined to pick up the textbooks and amass the baseline scientific knowledge (I'm sure they exist, although I haven't met them yet), they'd still lack the years of laboratory work and experience of applying this knowledge.
My original comment was apparently poorly worded because it was interpreted by the responders differently than I intended, but delightfully, it resulted in very thoughtful comments. I am very skeptical that one can make even small contributions to genetics without the experience of years of specialized work. There are ancillary problems that could be done by someone with a programming or CS background, e.g., a better LIMS system, or perhaps protocol management, but I don't see those tasks as leading to later making meaningful contributions to the field of genetics. The MD or PhD isn't required, but all the work done leading up to it is, and so as I see it those prepared to make the contributions are most likely going to have gotten the degree on the way.