Data Engineer vs. Data Scientist: What’s the Difference?

Data engineers and data scientists allow companies to implement strategies to leverage data and provide valuable metrics. In today’s world, data is widely accessible and is useful for optimizing business practices, such as maximizing profitability and improving the efficiency of business operations. However, data alone isn’t helpful. It takes professionals such as data engineers and scientists to turn large amounts of data into usable information. 

Depending on the size of an organization, data scientists may perform the role of both data engineers and data scientists, with data science being the broader field. Otherwise, these two professions often work together on the same projects, with data engineers supporting the architecture used by data scientists. Explore the key differences between a data engineer and a data scientist to understand each role better and which career path is right for you.

Read more: Your Guide to Data Science Careers (+ How to Get Started)

What does a data engineer do?

Data engineers design and develop the infrastructure to process, store, and analyze data. Their work makes accessing data much easier for data scientists, analysts, or other team members. Accessibility is important to the value of data and the insights it can offer, as is the quality of the data. Data engineers ensure that data is high quality to maximize its usability. 

High-quality data has specific characteristics, such as completeness, relevance, and accuracy. Data engineers also use the ETL (extract, transform, load) process, which allows data from multiple sources to integrate into one location or system, such as a data warehouse. ETL pipelines are a common form of data architecture, and data engineers can implement them to automate the process.

Your responsibilities as a data engineer can vary, with some focusing most of their work on databases and others spending more time engineering the data pipeline. Database data engineering involves creating or maintaining large, complex databases and finding ways to optimize performance. Pipeline data engineers focus more on transforming data to make it more accessible for analytics.

Read more: What Is a Big Data Engineer? A Career Guide

Data engineer skills

Data engineers need to possess various technical skills to perform job duties. Required skills typically include the following:

• Programming: Knowledge of programming languages is critical for designing and maintaining data architecture. Some of the common programming languages data engineers use are Python, Java, and Scala.

• Database and data warehousing systems: When working with databases, skills such as SQL allow data engineers to access and navigate databases. All that data also needs a place to be stored, which depends on data warehouses built by data engineers.

• Analytical skills: Working with data means data engineers benefit from being analytically minded to identify opportunities to improve systems and optimize the use of data.

What does a data scientist do?

Data scientists use a combination of technical methods and concepts to develop insights from data that allow organizations to make informed decisions. These techniques involve statistics, programming, machine learning, artificial intelligence, and other advanced analytics tools, such as predictive modeling. By applying industry-specific knowledge with their skill set, data scientists can deliver valuable information across multiple areas of an organization.

In some organizations, data scientists may also handle many of the responsibilities of a data engineer, such as managing databases and organizing data to ensure the use of only high-quality information. However, data scientists set themselves apart from data engineers when it comes to gaining actual insights from data and the data visualization component of data science, allowing the presentation of their findings in a simpler manner for less technical audiences. 

Data scientist skills

Data scientists have many of the same skills as data engineers since they perform the duties of both, depending on the employer. They may not have the same level of knowledge in specific data engineering areas, but overall, a data scientist's skill set tends to be more versatile. 

Some of the critical skills you should develop for a career as a data scientist include:

• Programming: Popular programming languages for data science include Python, R, Julia, SQL, and Scala.

• Data structures and algorithms: Knowledge of data structures and algorithms assists data scientists with data management and analysis. Machine learning algorithms can identify patterns within data and support the automation of data science processes.

• Data wrangling: Wrangling skills allow data scientists to transform raw data into usable data by removing outliers and incomplete or unnecessary data and merging data from multiple sources.

• Probability and statistics: Implementing techniques involving probability and statistics allows data scientists to analyze data and produce predictive models to identify future trends.

• Data visualization: Visualization tools such as Tableau are valuable skills for data scientists to develop. Typical data science programming languages like Python and R have libraries and packages to turn your data into graphic representations.

Read more: 7 Skills Every Data Scientist Should Have

Education requirements for data engineers and data scientists

Data engineer and data science education requirements are similar since some overlap exists in the responsibilities and skills of either role. Both positions typically require bachelor’s degrees in a relevant field. You could opt to study areas like computer science, data science, computer engineering, mathematics, and statistics. Additional education options include master’s degrees, bootcamps, and certifications. 

You can also pursue valuable Specializations and Professional Certificates, such as a Google Cloud Database Engineer Specialization or Google Data Analytics Professional Certificate. These are useful in developing some of the more specific and technical data engineering skills and data science skills you need to succeed.

Salaries and job outlook between data engineer vs. data scientist

According to Lightcast ™ Analyst, data engineers in the US earn an average yearly salary of $135,504 [1], while data scientists earn $114,282 [2]. Along with their high pay, data-related positions have a strong outlook over the coming years. The US Bureau of Labor Statistics projects a 35 percent growth in data science jobs from 2022 to 2032 [3] and an 8 percent growth for database architects, who share similar responsibilities to data engineers [4].

Getting started with Coursera

Data engineers and data scientists both play important roles in allowing their organizations to leverage data using similar skills, such as computer programming. However, these two positions are, in fact, different, with data engineers primarily building the architecture data scientists use. 

On Coursera, you can find highly rated courses to help you take the next step to becoming a data engineer or data scientist. Data Engineering Foundations Specialization from IBM offers an opportunity to develop your Python and SQL skills to manage databases and other fundamental data engineering concepts. 

Introduction to Data Science in Python from the University of Michigan is another course where you can learn the basics of Python programming and data manipulation techniques using the Python Pandas data science library.