I’ve been skimming Dice to look at the market lately. Of course, most job postings are written with a list of libraries, but I realized when the Wall Street Journal and I spoke about it in my posting on The Staffing Crisis I never mentioned the reason why this approach doesn’t actually help the companies seeking people.

I can appreciate that teams want someone that knows Spring Boot and RESTful API design. They might also want to know how to consume Kafka messages, and they might want to know how to use Redis for pubsub, streams, or state.

What does any of this have to do with dependencies?

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The Wall Street Journal has an article (unfortunately pay-walled) titled Companies Need More Workers. Why Do They Reject Millions of Résumés? by Kathryn Dill on Sep 4, 2021 talking in detail about many aspects of the way modern hiring practices actually discard a huge number of qualified candidates.

Of course, the main tool for implementing those aspects are automated matching systems. I did a bit of an experiment a few months back when I was being bombarded with offers for gigs that made no sense for me. I changed my resume in an intentionally skewed way to ensure that I violated the criteria used by most computer matching systems.

In short, I intentionally broke my resume months before the article. That meant I was an unintentional test of that article.

The results were not what I expected…

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Enterprise systems are very different from “applications.” It’s almost all because of scale, but scale is a qualitative difference — programs built to “enterprise” or “Internet” scale are not just bigger (key — “just”) but they are dramatically busier.

Consider a blog that receives 2000 “hits” (page-views) a day. That can be served effortlessly using any technology on any hardware platform, from Raspberry Pi to AWS or Azure. This doesn’t mean that the blog isn’t important, or valuable (it could be a blog that is used exclusively by CEOs and world leaders). But it means that the scale is not a technical challenge.

Contrast that with Facebook, Amazon, Google, Twitter, or Bloomberg. They have tens or hundreds of thousand of transactions that involve data being created and altered according to complex rules — per second. These are the companies that caused the growth of enterprise technologies, and that need them.

But how does a solution that needs to go to Enterprise/Internet scale start?

Hint: it’s not with a scalable infrastructure using Docker and Kubernetes with CI via Jenkins.

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