You’re hiring data engineers wrong. And your best candidates know it.
Most data engineering interviews test SQL puzzles and algorithm whiteboarding. The job requires debugging production pipelines at 2am and explaining to stakeholders why their data is late.
The gap is obvious.
Senior engineers with 10 years of experience sit through LeetCode challenges designed for fresh graduates. They know the real work is pipeline design, failure recovery, and stakeholder communication. The interview tests none of it.
Research from Google showed structured interviews have 26% validity in predicting job performance. Unstructured interviews? Just 14%. Most technical interviews fall somewhere in between-testing syntax recall instead of system thinking.
What actually predicts success:
- Walk through a past project. Where did it break? How did you fix it?
- Give a realistic problem. “This pipeline failed at 3am. Here’s the logs. What do you do?”
- Ask about trade-offs. “Why did you choose X over Y?”
The best data engineers I’ve worked with weren’t the fastest at inverting binary trees. They were the ones who could trace a data issue through five systems and explain it to a non-technical PM.
How many senior candidates dropped out of your pipeline last quarter?