Data Scientist and Beyond: Jobs in the Big Data World
Big Data is bigger than ever, and so is the job market for people who can help transform, store, analyze or otherwise work with big data. This article outlines the current job market and the types of Big Data jobs.
Hard statistics on the number of big data jobs available in 2017 are elusive. However, a quick look at the data that is available strongly suggests that the Big Data job market is strong and growing.
There were 1.9 million Big Data jobs in the United States, and 4.4 million worldwide, in 2015, according to Statista.
In spring 2017, IBM predicted that Big Data jobs in the United States would increase to 2.7 million by 2020, a 28 percent increase over current levels.
Big Blue also thinks that there will be 700,000 new positions per year in Big Data by 2020, and that average salaries for typical big data jobs hover around $110,000.
Big Data Jobs: 5 Common Positions
What, exactly, do these various jobs entail? And which types of skills should you acquire if you want to work in the Big Data world?
Following are examples of typical big data positions:
Data engineers architect and maintain platforms for storing and analyzing data. You could think of data engineer as the most general-purpose type of position in the Big Data field. To be a data engineer, you need to know a little bit about everything related to data analytics, transformation, and storage.
A data scientist specializes in interpreting data, usually in an automated fashion. Programming skills are a must, as is the ability to work with modern Big Data frameworks like Hadoop.
Machine Learning Engineers
This is a relatively new position that focuses on using data to power artificial intelligence. This job overlaps somewhat with data scientist jobs since data scientists analyze data in order to reveal insights.
However, as machine learning becomes more and more important in applications ranging from “learning” thermostats to self-driving cars, demand is rising for engineers who focus specifically on machine learning.
Storage engineers have been around for decades. They are the people who maintain databases and storage infrastructure.
As Big Data has grown into a discipline of its own, however, storage engineers have assumed special importance. Architecting and maintaining storage systems for petabytes of data requires special skills, and the ability to work with platforms like Hadoop.
Data Quality Engineer
Big Data isn’t very helpful if it lacks quality. Data quality engineers specialize in improving and maintaining the quality of data sets.
In many organizations, this work might fall within the role assumed by data engineers, but dedicated data quality engineering positions are now also starting to appear within organizations that recognize the extreme importance of maintaining data quality. Working as a data quality engineer requires expertise with the types of problems that degrade data quality, as well as the ability to use data quality tools.
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