What is data engineering, and why should you care about it?
Here's the simplest way to put it: data engineering is the work that happens before anyone can do anything useful with data. Someone has to pull it from source systems, clean it, transform it into something consistent, and land it somewhere it can actually be queried. That someone - or that team - is doing data engineering, whether they call it that or not. The reason this matters when you're hiring is that a lot of software companies will tell you they do it. Fewer of them genuinely do.Why does the wrong hire hurt more than it looks like upfront?
It rarely falls apart immediately. The first few weeks feel fine. Pipelines run. Dashboards populate. Then three months in, something starts behaving oddly - numbers that don't match, jobs that fail silently, latency that creeps up without explanation. By that point, the original team may be gone or mid-contract on something else. You're left debugging architecture you didn't build, in code you didn't write, for a system your internal team was never trained to maintain. The real cost isn't the rebuild. It's the decisions your business made on unreliable data while you were waiting to notice the problem.
