Workplace job titles are often far from accurate or precise. Many employees are quick to point out that their job titles don’t align with the work they actually do. Some companies even choose to forego job titles altogether, instead embracing the theory that everyone knows their rule – and sometimes to underscore the idea that hierarchies aren’t the best way towards innovation.
In technology in particular, things are little different. It might seem that anyone who works in tech is a programmer, or at least has some programming skills, but with big data on the rise, two jobs are in high demand: data engineers and data scientists.
The positions may sound the same – and companies may think they’re the same, with similar job descriptions or candidates. But, they’re very different, with less overlap than the names may imply.
As big data integrates into all types and sizes of companies, the positions of data engineers and data scientists are increasingly vital. Let’s explore what these job titles mean and how they support two different, both necessary, parts of big data.
In the last two years, the world has generated 90 percent of all collected data. Two years! That means two things: data is huge and data is just getting started. As such, companies are seeking employees who can help them understand, wrangle, and put to use the potential of big data. Data engineers and data scientists are increasingly vital to this effort.
A simple distinction, though not complete or always accurate, is that a data scientist is more math-oriented while a data engineer is more IT-minded. This correlates to necessary job skills: while data scientists and data engineers both possess some analytics and programming skills, the scientist has more advanced analytics skills and the engineer has higher programming capabilities.
But it may be the way these skills play out in the workplace that is the key difference. In order for a data scientist to perform data science, a data engineer must first create the structure and provide the data for the analysis. Data pipelines are a key part of data analysis – the infrastructures that gather, clean, test, and ensure trustworthy data. Depending on the business, data pipelines can vary widely: this is the data engineer’s specialty.
But once the data infrastructure is built, the data must be analyzed. Enter the data scientist.
Like scientists who tend to work in universities or R&D environments, data scientists often come from a more academic background. They may have degrees in math, statistics, physics, or a similar type of applies math, and they want to focus on analytics – the discovery, understanding, and communication of data patterns. The results of a data scientist’s work may be developing new algorithms or features, extracting data patterns, and visualizing data. At the farther end, a data scientist may build a machine learning model or a form of artificial intelligence.
But data scientists that are employed by companies don’t exist in a theory vacuum.
