Think Big Analytics CEO Ron Bodkin reveals what he looks for in candidates for Hadoop and other big data-related positions.
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Ask Think Big Analytics CEO Ron Bodkin what he looks for when vetting job candidates, and you might notice his first few answers appear to have little to do with Hadoop and other big data technologies. Rather, they focus on people.
"It's more art than science," Bodkin said of evaluating big data talent. In an interview, we asked Bodkin, among other things, what he looks and listens for when evaluating candidates for big data-related roles. Here's what he had to say.
Prioritize a person's potential over his past "Our basic philosophy is to find the overachievers who are super capable and have the right attitude," Bodkin said.
To do so, Bodkin and his team place more emphasis on so-called soft skills than on technology chops. Sure, if you want to be a top-notch data engineer, you're going to need to develop premium programming skills. Similarly, if you want to become a data scientist, a strong background in math and statistics wouldn't hurt. But the underlying skills are more tools for the job than predictors of success in Bodkin's eyes.
"...people [who] have proven they can learn new things, they can drive results, they work well on teams, they collaborate -- those are some of the key qualities that we look for," Bodkin said.
There will probably always be a seat at the table for the brilliant engineer more comfortable with machines than people; there will be many more seats reserved for those who pair sought-after IT skills with the ability to communicate and work well with others.
"We need to have people that are able to work with our customers... and communicate effectively and understand what they need," Bodkin said. The same ability would hold true with internal partners and stakeholders, too. "Almost anybody building a successful big data team [needs those people]."
Tech skills still matter People skills are big on Bodkin's list, for sure, and he believes that if you hire good people with the right aptitudes, you can train them on the nitty-gritty of Hadoop, Cassandra, Pig, HBase, Yarn, and other big data technologies. "We teach a lot of technical skills based on hiring the people with the right potential," he said.
Still, you can't ignore tech skills entirely. That CPA might be a whiz with numbers, but that doesn't make her a data scientist. Here are some of the things that Bodkin said he likes to see in a candidate's background, especially in combination:
Engineers with experience developing advanced, large-scale systems
Engineers who have worked with large, complex data sets
People who have worked in multiple industries
IT pros well-versed in the Java ecosystem -- not just the programming language, but in libraries and other areas
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