Data engineers vs. data scientists: The difference according to LinkedIn data

Data engineer and data scientist are the two most popular career tracks in big data. There are good resources explaining how these roles are alike and different, and how they work together (see the end of this post for a list). Stitch recently released a report based on self-reported LinkedIn data that adds new insight on this topic.

A list of LinkedIn skills reveals a clear difference between data engineers and data scientists. Take a cursory look at this chart and you’ll see that data engineers are concerned with data infrastructure, while data scientists are concerned with analysis:

Skill differences between data engineers and data scientists

It’s worth breaking this down a little further. A list of skills on a LinkedIn profile doesn’t differentiate between work projects, tools, and languages, but we can tease these out:

Work, tools, and languages

Understanding these roles using LinkedIn data is one helpful view on this topic, but as I said at the beginning, it’s not the only perspective out there. If you want to dig deeper into understanding career tracks in big data, here are some resources to keep you going:

  • This post by Alyssa Kwan is one of the most thoughtful perspectives you’ll find on this topic. Alyssa digs into one of the lesser discussed differences between these roles, education, and training, and explains how this difference creates problems down the road when these roles work together.

  • This post from Insight Data gives more context on where these roles need to develop knowledge and expertise. For example, data scientists need expertise in communicating and presenting the results of analysis, while data engineers need to build expertise in systems monitoring and data cleaning.

  • This post from Udacity compares data analysts, data scientists, and data engineers. As a bonus, it includes a Venn diagram showing how data engineers and data scientists are alike and different.

  • This post from Analytics Vidya looks at the differences between data scientists, data engineers, and statisticians. It includes a breakdown of responsibilities, skills, tools, salaries, and career paths for each role.

  • This post from Big Data University compares data analysts, business intelligence developers, data engineers, and data scientists.

(Click here to read the full report)
Click here to read the full report