Data Scientists, Data Engineers, Software Engineers: The Difference According to LinkedIn

The differences between Data Scientists, Data Engineers, and Software engineers can get a little confusing at times. Thus, here is a guest post provided by Jake Stein, CEO at Stitch formerly RJ Metrics, which aims to clear up some of that confusion based upon LinkedIn data.

As data grows, so does the expertise needed to manage it. The past few years have seen an increasing distinction between the key roles tasked with managing data: software engineers, data engineers, and data scientists.

More and more we’re seeing data engineers emerge as a subset within the software engineering discipline, but this is still a relatively new trend. Plenty of software engineers are still tasked with moving and managing data.

Our team has released two reports over the past year, one focused on understanding the data science role, one on data engineering. Both of these reports are based on self-reported LinkedIn data. In this post, I’ll lay out the distinctions between these roles and software engineers, but first, here’s a diagram to show you (in very broad strokes) what we saw in the skills breakdown between these three roles:

Data Roles and Skill Sets
A comparison of software engineers vs data engineers vs data scientists

Software Engineer

A software engineer builds applications and systems. Developers will be involved through all stages of this process from design, to writing code, to testing and review. They are creating the products that create the data. Software engineering is the oldest of these three roles, and has established methodologies and tool sets.

Work includes:

  • Frontend and backend development
  • Web apps
  • Mobile apps
  • Operating system development
  • Software design

Data Engineer

A data engineer builds systems that consolidate, store, and retrieve data from the various applications and systems created by software engineers. Data engineering emerged as a niche skill set within software engineering. 40% of all data engineers were previously working as a software engineer, making this the most common career path for data engineers by far.

Work includes:

  • Advanced data structures
  • Distributed computing
  • Concurrent programming
  • Knowledge of new & emerging tools: Hadoop, Spark, Kafka, Hive, etc.
  • Building ETL/data pipelines

Data Scientist

A data scientist builds analysis on top of data. This may come in the form of a one-off analysis for a team trying to better understand customer behavior, or a machine learning algorithm that is then implemented into the code base by software engineers and data engineers.

Work includes:

  • Data modeling
  • Machine learning
  • Algorithms
  • Business Intelligence dashboards

Evolving Data Teams

These roles are still evolving. The process of ETL is getting much easier overall as new tools (like Stitch) enter the market, making it easy for software developers to set up and maintain data pipelines. Larger companies are pulling data engineers off the software engineering team entirely in lieu of forming a centralized data team where infrastructure and analysis sit together. In some scenarios data scientists are responsible for both data consolidation and analysis.

At this point, there is no single dominant path. But we expect this rapid evolution to continue, after all, data certainly isn’t getting any smaller.

14 thoughts on “Data Scientists, Data Engineers, Software Engineers: The Difference According to LinkedIn”

  1. Software design made by Software Engineer is based on the requirements identified by Data engineerr or Data scientist. Data engineering and Data science both emerged as niche skill set within software engineering. All data engineers or data scientists were previously working as a software engineer, the architecture of software in Software design phase, can not be assessed without pre analysis of data, done by Data engineers or data scientists.

    1. “All data engineers or data scientists were previously working as a software engineer”… I do not completely agree with that statement. I know of some data scientists who have psychology backgrounds or stats backgrounds. However, the disciplines you mention do need a good understanding of data.

  2. The skill set between Data Engineers and Data Scientist is very different. Although both roles need to hack data, the Scientists need that strong background on Math and Statistics to be able to best use Machine Learning resources, define the benchmark models to use and overall lead the Analytics discussion with BDMs

  3. I think it is astounding how much data we can collect with the technology we have nowadays. It’s important that that data is understood correctly and in the right context by data scientists. I agree that more training is required as we gather more data in our technology saturated era.

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