How to Hire for Technical, Data-Centric Roles

Data Engineering| Team| hiring | | Claire Carroll

Unemployment is low, and the demand for skilled workers is high in many industries. That means top tech talent is more in-demand than ever, and 87% of hiring managers find it difficult to get the engineers they need. So how can companies scale, hiring at the level they need to become a data-driven enterprise?

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Who to Target

Reaching out to candidates who already have jobs, known as passive candidates, may take more recruiting energy, but can vastly improve your applicant pool. It seems like common knowledge, but remember that top talent has likely already found a home. Using referral systems can help, but Dataiku also leverages dedicated mailings.

“I try to reach out with really specific emails that target the candidate’s history and interests,” says Dataiku Technical Recruiter Maïté Le Goadec, “ Buzzwords like artificial intelligence and machine learning help too, but at the end of the day, we rely on the strength of our product and the good development team environment to draw candidates.”

If you limit your search to only graduates from top colleges, you risk missing out on great talent and perpetuate homogenous teams (plus you might have to pay more per candidate). Consider top candidates from diverse educational backgrounds, including those who took time away from the workforce for their families or older applicants, whose experience is historically undervalued.

Leveraging data to better target your outreach can definitely make you more efficient. For example, if you need your engineers to relocate to Austin, you can filter on who’s moved for a job recently. While improving the quality of your talent is always worth it, you should evaluate if you are growing fast enough to warrant the costs to access this data (and determine if you can capitalize on it).

How to Interview

Traditional interviews may not be the right solution. They’re relatively easy to prepare for, which means that sub-par candidates may say all the right things anyway.

Microsoft recruiters recommend that instead of using abstract thought problems or “surprising” candidates with questions about standard data structures, you should instead present a problem that your dev team is actively trying to solve.

By giving candidates access to relevant data and enabling them to gather more resources and review in advance, you can see what a candidate would actually be like on the team; surprising them won’t lead to their best work and won’t accurately reflect your dev environment anyway.

collaborative interview

Maïté also makes sure to take social and communication skills into account. If the best engineer out there can’t explain their personal projects to a non-technical recruiter or marketer, then they’ll likely have a hard time communicating and relating to clients or business users on different workstreams (a common scenario in data departments and for data teams embedded within any company).

How to Retain Talent

tIf you’re poaching engineers, chances are your competitors are trying to do the same to your dev or data team. So you’ll not only need to wow engineers at the interview, but also - importantly - keep them happy and challenged once they start.

One common way to retain engineers is through social initiatives and benefits, like free food or team outings. But arguably more important is providing data teams with the proper tools and trainings that allow them to be productive. In fact, Dataiku CEO Florian Douetteau explains why tools and trainings often outweigh fancy and trendy perks in the long run.

Educational initiatives are another critical tool; investing in engineers’ growth shows that you care about their personal development and are committed to keeping them at your organization.

employment win

When hiring, you have to be wary of growing pains and team culture shifts as well. Grab a copy of our free white paper on how to scale data teams for insights into best practices to improve team morale and strength.

six challenges to building a successful data team

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