Wednesday, Jul 10 2019

The 3 Major Differences Between Data Engineers and Data Scientists

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The 3 Major Differences Between Data Engineers and Data Scientists


Having the right positions represented on a team is critical to a company’s success. Similarly, if you are a professional, understanding what various job titles mean is crucial for career planning, as many that sound alike are actually very different.

At times, data engineer and data scientist are used interchangeably, mainly because they have skills associated with Big Data. However, these two roles have distinct differences, so using one title in place of the other can lead to confusion, skills gaps on a team, or a career that isn’t what you pictured.

If you want to make sure you are using the right job title, here are three major differences between data engineers and data scientists.

  1. Function

The purpose of data engineering and data science roles are vastly different. Data engineers tend to be responsible for the creation of solutions, such as software that is used to manage data.

Data scientists, on the other hand, are mainly responsible for analyzing data. They are more commonly tasked with using solutions to access valuable insights that can assist the business. Additionally, they need to be able to relay this information in a way that all key stakeholders can understand, including those with a variety of knowledge levels.


  1. Knowledge Bases

Data engineers have a strong programming background. Usually, they have skills in languages like Java, Python, and Scala. Typically, their skills are somewhat specialized, focusing on areas like Big Data and distributed systems.

As a result, data engineers can create software solutions that serve Big Data initiatives. They also have the ability to build data pipelines and have a deep understanding of a range of technologies and frameworks. While data engineers may also have some novice to intermediate data analysis skills, that is not a core focus on their career.

In contrast, data science professionals typically have a background in mathematics and statistics. In some cases, applied math skills are also critical, particularly if the role involves artificial intelligence or the creation of machine learning models.

While many data scientists do have some programming skills, these are usually developed over time and only when necessary. It is not a core focus, so they should not be considered to be programmers.


  1. Business Acumen

Data engineers are usually somewhat distanced from the business aspects of operations. While they may have a reasonable overview, their duties don’t have to take the organization’s business goals into consideration nearly at the level that data scientists need to consider. Instead, these professionals concentrate on providing functional solutions that can manage the data and support data-based initiatives, ensuring others can derive the insights they need with ease.

Data scientists usually need a solid understanding of business. Since they need to provide insights that can help the organization, understanding how the business world and company works are essential. This ensures they focus on the right areas and can choose pathways and concentrate on areas that provide the most value.

Ultimately, data engineers and data scientists are not the same. By understanding the differences, you can make sure that your team has the right professionals or ensure that your career is heading in your preferred direction.


Make Your Next Career Move with the Help of The Armada Group!

If you’d like to know more, the staff at The Armada Group can help. Contact us to speak with one of our skilled recruitment team members today and see how our tech role expertise can benefit you.