Good for you. But I think you're being uncharitable by failing to distinguish between "concept I didn't understand" and "thing I forgot to consider until I saw the problem it caused". The title also suggests the former, but I think the author is being a bit humble by underplaying his existing knowledge. Likely he actually did know what indexes are before; if you asked him to detail how MySQL foreign keys work he might have even remembered to say they add an implicit index. But it's super easy to miss that you're depending on a side effect like that until you see the slow query (or, in this case, high bill).

When you're programming, how many compiler errors do you see a day? (For me, easily dozens, likely hundreds.) Do you think each one indicates a serious gap in your knowledge?

Along these lines: imposter syndrome is a common problem in our industry. One way it can manifest is junior engineers can thinking they're bad programmers when they repeatedly see walls of compiler errors. I think it'd help a lot to show them a ballpark of how often senior engineers see the same thing. [1] I know that when I'm actively writing new code (especially in languages that deliberately produce errors at compile time rather than runtime), I see dozens and dozens of errors during a normal day. I don't think this is a sign I'm a bad programmer. I think it just means I'm moving fast and trusting the compiler to point out the problems it can find rather than wasting time and headspace on finding them myself. I pay more attention to potential errors that I know won't get caught automatically and particularly to ones that can have serious consequences.

I think the most important thing the author learned is that failing to add an index can cost this much money before you notice.

Ideally the author and/or the vendor will also brainstorm ways to make these errors obvious before the high bill. Load testing with realistic data is one way (though people talk about load testing a lot more than they actually do it). Another would be watching for abrupt changes in the operations the billing is based on.

[1] This is something I wish I'd done while at Google. They have the raw data for this with their cloud-based work trees (with FUSE) and cloud-based builds. I think the hardest part would be to classify when someone is actively developing new code, but it seems doable.