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How Do Data Scientists and Data Engineers Work Together?

Data scientists and data engineers are commonly confused by beginners without any significant experience in data science. And while their jobs might seem similar at a glance, there are actually some significant underlying differences. If you’re considering a career in data science, it’s important to understand how these two fields differ, and which one might be more appropriate for someone with your skills and interests.

What Does a Data Scientist Do?

Data scientists are involved with the direct analytical side of things. They work on models to process data, propose solutions to specific problems, and explore the limits of the data science domain to look for appropriate ways to tackle challenges. The work of a data scientist involves a lot of math and a deep understanding of the statistical concepts behind data science. A strong mathematical and statistical background is necessary to progress as a data scientist, and even to get hired by a reputable company.

What Does a Data Engineer Do?

A data engineer, on the other hand, is more concerned with the actual technical implementation of solutions. Once a scientist has come up with a model, it’s up to the engineer to figure out how to integrate it into the overall data processing pipeline. Data engineers have to be careful to maintain a balance between accessibility, flexibility and performance of the systems they work on.

They also have to understand the tech stack they are working with as completely as possible. When a solution is to be implemented, it’s up to the data engineers to determine what languages, databases, and other pieces of technology should be used to put together the final result. A good deal of scripting is usually required to tie everything together.

How the Two Roles Work Together?

A good way to look at data scientists and data engineers is via the analogy of architects and civil engineers. Architects are the ones that come up with the initial plans, while engineers implement them while observing structural limitations and other similar points. It’s not too different in the world of data science. Data scientists plan and data engineers build and implement.

The two roles work closely together to come up with the final solutions though. It’s important to have good communication skills on both sides, because it’s often necessary to consolidate ideas and limitations, and this has to be done in a way that doesn’t undermine anyone’s involvement in the project. Good pairs of data scientists and engineers can prove invaluable in the chaotic environment that this work is usually done in.

Which Career Path Is Right for You?

Choosing whether you want to work as a data scientist or data engineer is important if you want to get involved with data science in general. If you enjoy math and exploring the theoretical concepts in the field, working as a data scientist might be more suitable for you. You’ll need a good understanding of statistics, linear algebra, and various other mathematical fields. You’ll also need to go through lots of published papers to gain a good understanding of how the field is tied together as a whole.

On the other hand, if you like to “get your hands dirty”, and often find yourself writing scripts to automate your work, rearranging parts of a pipeline to make it more efficient, and worrying about technical limitations, then working as a data engineer might be right up your alley. This is a very technical field, and you don’t necessarily need a good understanding of the mathematical basics to be successful in it. It can definitely help though.

Why It’s Worth Familiarizing Yourself with Both Sides

No matter which side you choose, it’s still a good idea to spend some time familiarizing yourself with concepts on both ends. A good data engineer must have at least some idea of how the models they’re implementing came to be in the first place, while a good data scientist must be aware of the rough limitations they can expect to encounter. That’s why the best specialists in those fields usually try to invest at least some effort into learning how the other side works. This will also prove very useful when trying to communicate a difficult concept.

Whether you’re going to focus on one side first and then pick up the other later, or if you’re going to spread your attention between the two initially is up to you. Both approaches can work, and it’s a matter of personal preference. Whatever track you choose, Springboard has offerings in both that can deepen your understanding and get you job ready in the field you’re interested in. Springboard’s Data Science Career Track is a good place to start your education if you are interested in that side of things.

 
 
Riley Predum has professionally worked in several areas of data such as product and data analytics, and in the realm of data science and data/analytics engineering. He has a passion for writing and teaching and enjoys contributing learning materials to online communities focused on both learning in general as well as professional growth. Riley writes coding tutorials on his Medium blog.