Totally depends on the org. I've worked as the 3rd employee of a months old startup and the biggest pain point was we owned no infrastructure and I did everythi

Totally depends on the org. I've worked as the 3rd employee of a months old startup and the biggest pain point was we owned no infrastructure and I did everything on my own computer, which was easy but limiting because were heavy into statistical modeling and needed more powerful compute, which we got but it took a few months. I've worked on a military/intelligence project that had been in operation for over 40 years. The oldest code had no commit history because it predated any modern VCS. Whoever wrote it was long retired if not dead. Pieces were spread across thousands of repos on many different forges. With large old-school Java OSGI and now with Kubernetes, which is basically Java written in Go, it's the abstraction and dependency injection. There is no possible way to tell until runtime what code is actually going to be used. Knowing the codebase means nothing if you can't mentally track where data flows because you don't know what the implementation is actually going to be from the code alone. With extreme HPC, it was damn near literally everything being bespoke. Custom filesystems. Custom network stacks. Expectations from running workloads on regular computers and software everyone else uses were always being subverted. With cloud environments, it's not having physical access to the machines. With on-prem labs, it's needing to provision and troubleshoot the physical layer. Either way has its drawbacks. With customer-facing software, it's customers not knowing what they want and not even knowing what they don't want until they have it, which turns out to be what they didn't want. With software for modeling and understanding the real world, it's that physics turns out to be really f'ing complicated.