I've been on both sides of the hiring table for data engineering roles. I've given take-homes, graded take-homes, argued with other panelists about take-homes, and done my share of them as a candidate. So when I tell you the entire system is broken in a way nobody wants to talk about honestly, I'm not theorizing. I watched it happen in real time.
Here's the situation: 64% of companies now prohibit AI tools in technical interviews. Meanwhile, 35% of candidates are using LLMs anyway, up from 15% just six months prior. In purely technical roles, that number climbs to 48%. And 61% of those candidates pass the approval threshold and advance without anyone noticing. The ban exists on paper. In practice, it's a suggestion that penalizes the people who follow it.
The Honest Candidate Tax
This is the part that actually pisses me off. If you're a data engineering candidate who follows the rules, who sits down with your take-home and writes your own SQL, builds your own pipeline, tests your own edge cases, you are now competing against people whose submissions were polished by an LLM in a fraction of the time. And the hiring team cannot tell the difference.
Cheaters have a roughly 3:1 pass rate advantage. That's not a guess; that's from Fabric's analysis of 19,368 interviews between July 2025 and January 2026. Candidates using AI tools scored above the 7.0 approval threshold 61% of the time. The honest candidates? They're producing slower, rougher, less polished work. Because that's what real human output looks like when you're solving an unfamiliar problem under time pressure.
