Roberts, Phillips and other experts say the skills most often mentioned in connection with big data jobs include math, statistics, data analysis, business analytics and even natural language processing. And although titles aren't always consistent from employer to employer, some, such as data scientist and data architect, are becoming more common.
A curious mind is key
As companies search for big data talent, they're tending to target application developers and software engineers more than IT operations professionals, says Josh Wills, senior director of data science at Cloudera, which sells and supports a commercial version of the open-source Hadoop framework for managing big data.
That's not to say IT operations specialists aren't needed in big data. After all, they build the infrastructure and support the big data systems.
"This is where the Hadoop guys come in," says D.J. Patil, data scientist in residence at Greylock Partners, a venture capital firm. "Without these guys, you can't do anything. They are building incredible infrastructure, but they are not necessarily doing the analysis."
IT staffers can quickly learn Hadoop through traditional classes or by teaching themselves, he notes. Burgeoning training programs at the major Hadoop vendors are proof that many IT folks are doing so.
That said, most of the jobs emerging in big data require knowledge of programming and the ability to develop applications, as well as an understanding of how to meet business needs.
The most important qualifications for these positions aren't academic degrees, certifications, job experience or titles. Rather, they seem to be soft skills: a curious mind, the ability to communicate with nontechnical people, a persistent -- even stubborn -- character and a strong creative bent.
Patil has a Ph.D. in applied mathematics. Sacheti has a Ph.D. in agricultural and resource economics. According to Patil, the qualities of curiosity and creativity matter more than one's field of study or level of academic credential.
"These are people who fit at the intersection of multiple domains," he says. "They have to take ideas from one field and apply them to another field, and they have to be comfortable with ambiguity."
Wills, for example, took a circuitous path to the role of data scientist. After graduating from Duke University with a bachelor's degree in math, he pursued a graduate degree in operations research at the University of Texas on and off while working for a series of companies before dropping out to take a job at Google in 2007. (He notes that he did eventually complete that master's degree.) Wills worked at Google as a statistician and then as a software engineer before moving to Cloudera and assuming his data science title.
In short, big data folks seem to be jacks of all trades and masters of none, and their greatest skill may be the ability to serve as the "glue" in an organization, says Wills. "You can take someone who maybe is not the world's greatest software engineer [nor] the world's greatest statistician, but they have the communications skills to talk to people on both sides" as well as to the marketing team and C-level executives, he explains.
"These are people who cut across IT, software development, app development and analytics," Wills adds, noting that he thinks such professionals are rising in prominence. "I'm seeing a shift in value that companies are assigning to these people," he says.
Sacheti, too, keeps his eye out for people like that. "We are finding there are a lot more who are flexible in learning new skills, willing to do iterative design and agile thinking," he says.
Roberts agrees. "The innate characteristics of people, like a predisposition to curiosity, can be more predictive of someone's performance in a role than them having a degree in, say, IT or IS or CS," she says.