As a data engineer, I think blaming Excel for this is the wrong framing. The real problem here is with the entire process, and specifically, the lack of testing. Excel is great for many use cases, especially if you need people to enter data somewhere. Its UI is unparalleled in terms of quickly giving something to users that they can understand, mess around with, and verify. It's a very common use case to then need to…
I don't think data engineering is hard or needs much resources. It does need people who care and who will think about things. Anyone who comes at a problem with the mindset 'this is going to he hard' probably lacks experience and will throw big-data frameworks at it, really screwing things up. The most significant, and valuable, resource needed is thought first, and knowledge+experience second. All IMO anyway.
You can write a simple test to check a function is working correctly, but how do you make sure your 100,000 item database doesn't have corrupted or missing data caused by the latest pipeline update, especially if the corrupted parts are rare?