What Are the Differences Between a ‘Data Scientist’ and a ‘Data Engineer’ (and Which One Do You Need?)

As data becomes more important to doing business, companies are finding themselves in need of various data-related workers. Sometimes, the titles aren’t clear, and it’s difficult to determine whether you need a data scientist, a data engineer, or perhaps both. Here are the descriptions of each one, as well as where the two jobs overlap.

Data Scientist

Data science is what happens inside a data infrastructure. Data engineering is what puts the data into the infrastructure to begin with.

The data scientist is in charge of deriving and delivering insights from the data. The data scientist collects the data, develops models based on the data, and builds a story out of the data, according to the insight it provides on a given topic. The data scientist generally interacts with both executives of the company and the clients, as relative to delivering insights from the data. This person needs to enjoy scrubbing sets of data for better insight and understanding. Their chief goal is to produce products from the data and create and deliver presentations to showcase those deliverables.

Data Engineer

The data engineer is more focused on designing, building, and maintaining the systems used to store and process all that data. The data engineer is in charge of building and deploying storage solutions that are able to handle the needs of the specific types of data the organization collects, stores, and processes. In many cases, the data storage has to be able to deliver analysis and insight in real time from data as it streams in. In other cases, there are massive quantities of large and unstructured data sets, such as video files, to be managed. Other situations might call for a data storage infrastructure that can handle frequent data access by large numbers of users. The data engineer has to be able to assess the data, the usage needs, and the applications involved, and determine the best architecture to meet the needs of that organization. The chief goal of the data engineer is to assure that the data is stored properly and made available to the data scientist and other analysts or users as needed. This job title calls for strong software engineering skills, programming skills, and other hard technical skills.

Work That Fall Under Both Job Titles

In most cases, it takes a full team of scientists and engineers to manage any significant quantity of data.

There is, however, sometimes overlap in these two positions, depending on the structure of the IT department and the business. In many cases, but the data scientist and the engineer will need to be able to handle the mathematics and statistical calculations involved in analyzing the data. Both might also need to be able to program for databases and perhaps applications, as well.

As you can see, “data scientist” is not a catch-all term to refer to any job title involving work with data. While smaller organizations might be able to get by with one or two working on databases and analytics, any data initiative that is of a reasonable size needs a team of data professionals, some in charge of the infrastructure and architecture, others responsible for the analytics and delivering insight from the data.

As the field of data science matures, expect even more differentiation to emerge among job titles relative to working with, collecting, storing, securing, and using different types of data. Ready to learn more about big data and what it takes to be successful with data today? Bring Big Data Week to your city.