Do Data Engineering Candidates Really Know Anything?

A framework for honestly evaluating programming skills, technical proficiency and conceptual knowledge.

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Once, after helping a client with a resume, I received an angry phone call. After receiving help they applied for a job. And, thanks to the changes we made together, scored an interview. But there was a problem. The interviewer asked whether the applicant knew an obscure Adobe program.

Admittedly, the client gave the perfect response: “I don’t know it but I’m a fast learner and, if given the opportunity, I’d be happy to learn prior to my start date.” There was (what I imagine to be) an uncomfortable pause.

The interviewer responded: “Oh… if you don’t know it, why is it on your resume?”

This is the worst nightmare for someone working in a highly technical field like data science, where seemingly everyone needs to know everything at every skill level. Such a discrepancy between what a hiring manager is looking for and what you’re actually comfortable admitting can lead to uncomfortable situations from deflecting interview questions to bombing white board sessions.

Which begs the question: How do you actually know when you really know a programming language, orchestration tool or data modeling technique?