Building a Big Data Team? Learn the Difference Between Data Scientist vs. Data Engineer vs. Data Analyst

Big Data means big work.

In 2015, Big Data could create 1.9M IT jobs in the U.S. alone, according to market analysts at Gartner, Inc.

In the meantime, there are at least three big-paying Big Data jobs out there that could offer qualified candidates salaries ranging between $89,167 to $144,777, according to recruiting data from CyberCoders.

Read on to get clarification of the three overlapping, but distinct jobs:

1. Data Scientist

It’s a relatively new role with a bright future. Once you read what data scientists do, you’ll see that landing this job could generate a lot of cool cred’ as well as a decent income.

The data scientist only came into prominence with the most recent generation of analytics.

“The data scientist figures out how to recommend products for you on Amazon, how to order the posts in your Facebook stream, and how to suggest the next music track in Pandora,” says Bob Moore, CEO, RJ Metrics, a big data analytics firm. These candidates are in very high demand.

A talented data scientist has a solid foundation in statistics and understands machine learning and algorithms.

The data scientist has often conducted and published research in the academic or corporate arenas.

“The data scientist likely has programming skills and definitely has strong statistical and analytical skills (SQL, R, STATA, etc.),” Moore says.

*Average Salary: 

Los Angeles | $117,857 New York | $135,357 San Francisco | $144,777

2. Data Engineer

Data engineers take very large data sets and format them so that their colleagues can analyze them.

To do that, data engineers assemble and maintain Big Data technology and the hardware infrastructure behind it.

“Google has lots of software and countless servers powering its services; the data engineers are the ones who build and maintain all of it,” says Moore.

The data engineer will have all the programming skills required by Amazon or Google such as OO (Object Oriented) design and expertise with algorithms.

“They will likely work with Hadoop, MapReduce, Storm, and all the other Big Data technologies out there, depending on the needs of the project,” says Moore. Because this field changes rapidly, it is critical to have experience working with a given technology, to have mastered core engineering skills, and to be able to learn quickly.

*Average Salary: 

Los Angeles | $125,000 New York | $122,750 San Francisco | $138,438

3. Data Analyst

Data analysts use data to make decisions.

They work with business units—marketing, sales—to translate their data needs into analysis.

“Analysts apply a range of skills such as with domain-specific tools like Google Analytics and with broader tools like Excel,” Moore says. The best data analysts have a firm foundation in statistics.

The data analyst will often have functional business skills in marketing, finance, and operations so they can understand the problems that the business units they serve face.

“They layer on strong Excel and SQL knowledge and always push the limits of available tools, since the landscape is constantly changing. The best analysts will use statistical methods, and tools like R and STATA,” Moore says.

*Average Salary: 

Los Angeles | $96,250 New York | $106,500 San Francisco | $89,167

*Average salary is compiled by employment data from CyberCoders between 2012-2014. 

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This article was written by David Geer, technology writer and journalist.