Big data engineers are in high demand, but this wasn't the case until relatively recently.
While scientists and IT professionals have always experimented and conducted business dealing with large amounts of data, the term “big data” didn’t even hit mainstream until April 1998 when John R. Masey, Chief Scientist at SGI, presented a paper titled “Big Data… and the Next Wave of Infrastress.”
Initially called “data engineers” in the early 1990s, the big data engineer job as we know it has only recently gained in popularity as a profession. Now, every mid- to large-sized company with a robust IT department wants to have someone on staff with “big data” in his or her title.
What does a big data engineer do?
The amount of data being processed and analyzed daily is staggering. IBM estimates that people create 2.5 quintillion bytes of data every day. The Internet of Things means almost anything that can be connected to the Internet either is or soon will be, and those devices produce more data than most companies can ever hope to analyze.
Enter the big data engineer.
A big data engineer is responsible for transforming large amounts of raw data into actionable intelligence for business decision-making, strategy-setting and innovation. It’s a big job, and it includes retrieving, interpreting, analyzing and reporting on a company’s data, which often must be gathered from disparate sources.
A big data engineer is usually responsible for setting up a business’s software and hardware architecture, along with the systems and processes users need to work with that data. Big data engineers not only need to have IT skills and usually a degree in computer science, but also mathematics and database experience as well.
Salaries and skills
The demand for big data engineers is strong. In fact, the 2019 Robert Half Technology Salary Guide reveals that big data jobs remain in great demand, along with web development and systems engineering positions.
The salary midpoint for big data engineers in the United States is $155,500. The position often requires a bachelor’s degree in a related field and four to six years of experience.
Skills and experience required for this role include:
- Strong knowledge of statistics and programming experience, ideally in Python or Java.
- The ability to design and implement solutions for big data challenges.
- Knowledge and experience in data mining and processing large amounts of raw data.
- Designing and maintaining relational databases for storage and data acquisition.
- Experience with NoSQL.
- Ability to communicate directly with business users and data scientists to understand objectives and create data processing workflows.
This post has been updated with more current information.