The Death of the Full-Stack Data Engineer

Stop hiring full-stack data engineers. They don’t exist, and pretending they do is burning your team out.

Between 2020 and 2022, “full-stack data engineer” showed up in every other job description. Companies wanted one person to do ingestion, transformation, analytics, ML, and infrastructure. The unicorn hunt was real.

Here’s what actually happened: you hired smart generalists, burned them out, and ended up with mediocre everything instead of excellent anything.

The field has matured. Platform engineers, analytics engineers, ML engineers - these are genuinely different skill sets with different depth requirements. Pretending one person can cover all of them is like asking your backend developer to also do mobile and DevOps. Some can. Most shouldn’t.

What works: T-shaped engineers. Deep expertise in one area, working knowledge across others. Then compose your team for coverage.

Hire for depth. Build breadth through team composition, not individual job descriptions. Your “full-stack data engineer” posting is probably why your best candidates aren’t applying.

Are you hiring for unicorns or building a balanced team?