It's not a test of grit. git happens to exemplify -- as well as any system I know -- many aspects of good data engineering. If you're into data and ML, those ar

It's not a test of grit. git happens to exemplify -- as well as any system I know -- many aspects of good data engineering. If you're into data and ML, those are things you ought to know too. For a data/ML position, in most cases, I'd expect you to be able to handle data cleanly and efficiently. If you can't, there are jobs far over on the data side, but: 1) As a business data analyst, you're fine with Excel and PPT, but you'll be paid roughly 1/3 of an ML/SWE position, and you should have excellent communication skills. 2) There are primary mathematical positions, where you work with a data engineer, but you'd better be awesome at math. AND it still helps to be able to handle data cleanly. Even so, good data workflows require knowing what you did, when, and to which version of data. Properly used, git provides an archival log of some of that. I use very similar data structures when I build some of my own data pipelines too, with data stored under its hashes, Merkle trees, DAGs, and similar. If you find that "annoying and time-consuming," I'd hire you for a business data analyst, and not much more. It sounds like you find that stuff boring, though. It's a test of interest, passion, and drive, much more so than diligence and grit. Although those are important too.