If you look around the career pages of tech or media companies, there are always open jobs in data. And this is despite the fact that data is not usually the large technical department (general engineering is usually several folds larger, unless you’re a pure data product company, and even then). 

But two different trends are fueling the faster growth of data teams: 

• The maturity of companies of all stripes in understanding the value they have in creating more data,and the value they would find in using it to further inform their business.

• The overall complexity and richness of the tools at our disposal also require larger teams. Whether you put data engineering in data, our data platforms are generally far more complex than what data engineering may have meant 10 years ago. Back in the day, data was really “just” business intelligence. And once you had ambitiously tagged your Web properties and apps and had signed a check to Omniture, you’d staff a few analyst jobs and be on your way to dashboard paradise. Such simple days.

So with demand being, to use a technical term, “hot-hot-hot,” well, money talks. And company culture, too: We talked about this in the previous newsletter, but if you’re an in-demand data professional, where do you want to work? At a company that actually does treat data like the great asset it can be — or at a company where the data presentation is just a conversation prop?

But there is another way to improve our prospects to detect and attract talent — and that’s improving the hiring game itself. 

Whose resume makes it past the filters of our recruiters? 

How do we approach understanding credentials and experience when so much of data is generalist enough that professionals from a wide array of disciplines possess a number of the fundamentals? Statistics majors feel like shoe-in, but the wider field of applied maths — of which you could consider statistics to be a part — really does prepare you for a lot of data careers, being focused on modelling). 

Meanwhile, a large part of data engineering — in particular data architecture — is really a flavour of general software engineering, for which training and experience can come from the most academic experience for folks who formally trained as engineers to folks who are self taught and learn the trade by doing. 

At The New York Times, widening the potential pool of candidates was done in a manner that tried to control for the thing that really mattered: the measurable fit-for-hire quality of the candidates. This was done in two ways: 

• Try and limit resume bias.

• Detect candidates with the right objective knowledge, even if their background is less conventional.

“We have SQL tests,”  Kendell Timmers, the SVP and head of data and insights at The New York Times, explained at the Monte Carlo Impact conference last month. “So one thing people often worry about, particularly coming from an unconventional data background, is that people will take a look at what you majored in or what school you went to and that will affect your evaluation.”

The tests, she explained, are a way to help both the interviewer and the candidate get an anchor into something more dispassionate than how well-versed a candidate is at the particular kind of interview they are taking. 

“This points towards having consistent evaluations because everybody takes the same test. The test is evaluated the same way for everybody.”

Now, the evaluation being the same for everybody does require some level-setting. How we may judge an answer could have natural variance. Complex SQL problems usually have several paths that could produce a correct result. Which one is the best answer does rest in the eye of the evaluator, to an extent. 

“We have this rigorous process where the people who are serving as a SQL evaluator sit down on each other’s evaluations occasionally to calibrate and make sure they’re reading the same way,” Kendell explained. 

On the other hand, job interviews will also consider the whole candidate and their cultural fit for the team they are joining. In data, where the diversity of profiles and experience can vary a lot, this would mean the hiring panel is going to be able to appreciate the diversity of these candidates in the first place — something The Times pays a lot of attention to, Kendell explained. 

A last component of a successful hiring process, Kendell noted, is how important hiring is made to be for the team. Is it part of the responsibility of the specific folks who work in recruiting or is it a part of many of your team members’ responsibility? 

“You really have to prioritise hiring to hire. We all have so much going on. That it’s very easy for this to kind of slip off to the side. And it’s not enough for you to say hiring isn’t your goal. Hiring has to be first of all in the goals of everybody on your team who’s doing the hiring. They have to have hiring as part of their goals or they’re not going to make time for it even though it will help them,” Kendell said. She noted that whichever part of hiring team members participated in — as test evaluators, as panel members — their participation needed to be part of their evaluation so their effort would be recognided properly.

This also means, in turn, that hiring is a stated goal: a measurable, stated goal that gets recorded as such — and where participation is a meaningful part of this person’s evolution in their own career ladder.

“All of this has to happen or hiring is always going to be something that you do on the side. I honestly think if you need to build a team, this has to take priority even over almost all the work you still have to keep the site running... . This really needs to be almost the first priority for a fair number of people that make it happen.”

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