I think it gets skewed by the business / data population, which is a bunch of “engineers” wrapping simple Java and Python to massage data fro multiple databases into some common form for a data lake or whatever. This sub field hires like mad and is filled with relatively niche and mid to low skilled roles - but lots and lots and lots of them, most medium to large companies hire these roles. Higher end engineering is fairly rarified and under represented.

I also looked at the salaries and was baffled - I’ve not worked in companies with those sort of bands since 1995. At most FAANG and big tech orgs that are serious those averages are under entry level and have been for decades. At one bank I worked at new hires were being offered $250k out of college - 15 years ago.

So these numbers represent the iceberg roles - not visible if you’re in high end jobs and companies, likely even startups, but where most engineers are actually working.

The article also mentions where you may want to move and lists Portland. I live out here; there are next to no tech companies here. You'll need to get a remote job if you want to make that kind of money.

No this is good info, Learn AWS Route 53 if you want to command a high salary.

Maybe this was written by an AI? I feel like an editor with some tech knowledge would have laughed at those categories.

They do explicitly call out that this one has under 100 data points. There's a few people who are well paid and are running software on map reduce. That's all.

Thank you. I literally asked out loud "Map reduce?"

However because Kafka is in the top 5 of this list, and I know Kafka well, clearly it's right ;)

Same. In 4th position "Chef". Then in the "Highest Paying Jobs in Tech" chart we have the following entries "Back-end software engineer", "Full-stack developer" and "Software developer". I mean, who wrote that article? Can't take it seriously at all.

They also talk about "AngularJS" which is the name of the legacy Angular. They also put AR/VR/Metaverse/Web3 in the same basket (section best "fields")... what the heck

They kind of go out their way to highlight that the sample size that chose that option was low. Hardly worth throwing out the article simply because there was an outlier.

It’s challenging stuff! You have to remember to map and when to reduce! /s

I saw that too and my first thought was how old is this article?