There's probably been an infinite amount of information on the topic of tech interviews. And that's a no-brainer since there's a limited amount of resources that go into the process. As a result, bias slips through, crucial details are missed, and wrong hiring decisions are made.
Over the last year, I've been running a tech interview platform called CoreSkills, where the primary metric of the product has always been the quality of interviews. And in this article, I want to share one fundamental learning I discovered that helped me avert most of the pitfalls above.
It all starts with defining a sound evaluation rubric. For CoreSkills, the following structure has been working well: problem, assessed skills, score, and comments. This structure lets me stay focused on the parts that matter the most and ignore the noise.
To bring a bit more clarity on how it might look in practice, let's consider the following example:
| Problem | Assessed Skills | Score | Comments |
|---|---|---|---|
| Develop a REST API service according to a specification. | Python,Rest API,TDD,Backend | 5 | All clear. The service API works according to the spec and all related API tests pass. |
| Implement handling of faulty HTTP requests. | Error Handling,Python,Rest API, TDD, Backend | 5 | All clear. All related API tests pass. |
| Cover business functionality with unit tests. | Python, Unit Testing, Backend | 1 | No tests were provided and the candidate motivated he didn't have time to add them. |
| Implement a solution according to Acceptance Criteria. | Problem Solving | 3 | Overall, the problem is solved. The initially submitted version was missing certain details, but then after I pointed those out, the candidate updated the implementation. |
It's important to note that the content of the rubric depends a lot on your situation, and it usually takes a few iterations to get to an optimal structure. That's why it's so crucial to collect feedback from all involved parties on the corresponding aspects of the interview.
Would this structure work in your interviews? What other ways of consolidating interview feedback did you try?
Don't hesitate to leave your thoughts in the comments below.
Stay curious! ✌️
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