I work in this field at a large medical research institution. There is a significant amount of genomics analysis that occurs here on a day-to-day basis. The genomic processing pipeline work all falls directly into my group.

There is next to zero demand for tool development internally. I do it on the side of "normal" IT data management because I love high performance computing, algorithms, and multithreaded hackery. But even at my large, well-funded institution, there isn't a specific role where that is all that you do by design.

I do suck at marketing - meaning, despite having some success with big improvements in research tools that folks have definitely appreciated, no one comes to me asking for help with better engineering of genomic applications. Partly that is due to many researchers maybe only know R, so they will default to whatever packages are already available in Bioconductor, install those, and throw the resulting mash-up for their current research effort onto the compute cluster and simply wait for hours or days for the jobs to finish.

PIs are often insulated from software engineering problems too - if work is completed before the next bi-weekly meeting and update session, well, it must be ok.