By Neil Witten
According to 365 Data Science, the employment rate of data scientists is expected to increase by 36% from 2021 to 2031. In the case of data engineers, Zippia projected a 21% increase in job availability from 2018 to 2028.
Clearly, the evidence shows now is a great chance to land either of these roles! At AI/ML Jobs, we know a thing or two about the tasks, responsibilities, and requirements of data scientists and data engineers. After all, our service is the perfect place to browse thousands of AI and ML jobs from the U.S. and abroad.
In this guide, we’ll look in-depth at what it means to become a data scientist or data engineer before exploring the key differences between roles.
Let’s dive in!
In a rush? Don’t worry—here’s a quick overview of what we’ll be covering in the remainder of the article:
Now, let’s dive into the details!
Data scientists professionally collect, analyze, and interpret data to provide an organization with key information and metrics to ensure they can make better-informed decisions.
Data scientists work to explain various phenomena relevant to the organization. This might come in the form of testing hypotheses, exploring the unknown, and making sense of the noise, while employing scientific and advanced analytical techniques to find solutions to key questions.
Data scientists have high-end investigatory data analysis responsibilities that may also involve the use of machine learning and predictive modeling to make sense of vast arrays of data.
The roles and responsibilities of a data scientist can range significantly depending on the specific job in question.
The roles and responsibilities of a data scientist can include:
Certain skills and requirements apply to those seeking a data scientist position. These often include:
A data engineer’s primary role is to design, maintain, and optimize data infrastructure for purposes including:
A data engineer’s job includes establishing pipelines to analyze raw data and output usable formats for relevant colleagues (e.g., data scientists) to work with in order to achieve their goals.
Data engineers also utilize software engineering principles to manage key data elements of data science and data engineering. For example, a data engineer may need to work on data flow process automation algorithms. Ultimately, data engineers tend to work alongside data scientists to achieve a wide range of goals.
The roles and responsibilities of the average data engineer may include:
Companies usually expect the following from their data engineers:
While there are certainly similarities between data scientists and data engineers, there are tons of important differences—some small, some massive.
Let’s explore some of these differences to better understand both roles.
Data engineers work primarily with architectures (e.g., databases and large-scale processing systems) by developing, constructing, testing, and maintaining them. This contrasts with data scientists who focus on applying scientific principles to filtering and organizing data.
While not always the case, data scientists tend to work with data that data engineers have already cleaned and organized to a certain degree. Data scientists also often employ advanced analytics programs and a mix of ML and statistical methods for predictive and prescriptive modeling alike.
Data engineers are mainly involved in the “construction” of data systems, while data scientists are more responsible for the scientific analysis of the data. Engineers primarily focus on building data pipelines and creating solutions for data storage, while scientists provide insights to aid organizations in making smarter decisions.
While both roles feature a wide array of advanced responsibilities (highlighted by the fact that data scientists and data engineers tend to land three-figure salaries), the roles clearly differ in many key functional ways.
Data engineers and data scientists tend to specialize in various niche areas. Some of these niches cross over, however, it is those where they differ that help to clarify the distinctions between these job titles further.
Data engineers, on the one hand, may specialize in constructing data pipelines, computer vision, business intelligence, and machine learning. Data scientists (while they generally do specialize in machine learning and business intelligence—thus some crossover) also, often specialize in data visualization and data mining.
In general, data engineers specialize in creating key data infrastructure for organizations, while data scientists focus more on working directly with data to produce tangible results for the business.
Although different sources provide varying figures for the average salaries of data scientists and data engineers, most suggest that the former tends to earn somewhat more. Based on a sample from five sources, this seems to be the general trend.
According to Comparably, data engineers earn an average annual base salary of $107,096, while data scientists earn an average annual base salary of $128,789.
This difference is less fundamental than the responsibilities and specializations of data scientists and data engineers. Also, salaries will likely differ more not based on role but depending on location, company, and industry.
In this comparison, we’ve looked at the key differences between a data scientist and a data engineer. We discussed how their responsibilities and specializations can differ while also exploring the roles and requirements of each job title.
Data engineers are primarily responsible for infrastructure fundamentals and ensuring that data is clean, which allows data scientists to conduct advanced analysis using scientific principles. Data scientists, on the other hand, focus on providing business insights and solutions to problems that enable better-informed decisions.
If you’re ready to find your first or next data role, AI/ML Jobs is the ideal place to explore relevant jobs from the U.S. and across the world. You can use our service to find jobs based on top locations, skills, and salaries alike!