Well, sure but one person's "basic" is another's "esoteric," and "when you do" is something I think is relatively infrequent. I cared deeply about this sort of thing when I was writing code for HPC modeling of physical systems, but in very specialized and narrow context. Since I've left academia I have rarely encountered a problem where understanding implementation details behind complexity was really necessary.

Knowing the typical time and space complexity for an algorithm (without even understanding one bit about the implementation details) in a given category might be considered too trivial to be even basic, but I submit it's about the most "advanced" piece of knowledge for what a typical engineer really must know to write good, high quality and performant enough code.

Most can get by using a given language's library implementation of an "obvious" data structure and naive algorithms around it. For example, in Python, the "obvious" structure to map keys to values is a dict, and iteration over the keys or item tuples is a typical access pattern. An engineer doesn't need to know whether the dict insert or lookup is amortized O(1) or any other value, and he doesn't need to know the space required is O(n), etc. In almost every case he's likely to put it to use for the dict is fine.