Data Driven Hiring For Data Professionals With Alooba
Data Engineering Podcast
Using a Luba to Build a Luba
The original use case that we had was more around the higherng use case and understanding in our candidate skills. But what we now call e growth, which is understanding yorten's capabilities, has ended up really kind of taking off more than what i would have thought. Ands we've gone off and built out separate dashboards and front ends to display those insights in a different way for those different target audiences.
Summary
Hiring data professionals is challenging for a multitude of reasons, and as with every interview process there is a potential for bias to creep in. Tim Freestone founded Alooba to provide a more stable reference point for evaluating candidates to ensure that you can make more informed comparisons based on their actual knowledge. In this episode he explains how Alooba got started, how it is being used in the interview process for data oriented roles, and how it can also provide visibility into your organizations overall data literacy. The whole process of hiring is an important organizational skill to cultivate and this is an interesting exploration of the specific challenges involved in finding data professionals.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Tim Freestone about Alooba, an assessment platform for evaluating data and analytics candidates to improve hiring outcomes for data roles.
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Alooba is and the story behind it?
- What are the main goals that you are trying to achieve with Alooba?
- What are the main challenges that employers and candidates face when navigating their respective roles in the hiring process?
- What are some of the difficulties that are specific to data oriented roles?
- What are some of the complexities involved in designing a user experience that is positive and productive for both candidates and companies?
- What are some strategies that you have developed for establishing a fair and consistent baseline of skills to ensure consistent comparison across candidates?
- One of the problems that comes from test-based skills assessment is the implicit bias toward candidates who test well. How do you work to mitigate that in the candidate evaluation process?
- Can you describe how the Alooba platform itself is implemented?
- How have the goals and design of the system changed or evolved since you first started it?
- What are some of the ways that you use Alooba internally?
- How do you stay up to date with the evolving skill requirements as roles change and new roles are created?
- Beyond evaluation of candidates for hiring, what are some of the other features that you have added to Alooba to support organizations in their effort to gain value from their data?
- What are the most interesting, innovative, or unexpected ways that you have seen Alooba used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Alooba?
- When is Alooba the wrong choice?
- What do you have planned for the future of Alooba?
Contact Info
- @timmyfreestone on Twitter
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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