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

Key takeaways

Data engineers and data scientists have distinct but overlapping roles. Engineers design systems while scientists analyze and model data for insights.

• Data engineers and data scientists can earn a median total pay of $131,000 and $153,000, respectively [1,2]. 

• Sharing some similar job responsibilities, data scientists may perform the role of both data engineers and data scientists, with data science being the broader field.

• You can become a data scientist or data engineer with qualifying skills in areas like database management, programming, and machine learning.

Explore the inner workings of data engineering and data science careers. If you’re ready to start gaining valuable skills to help you extract value from data, then consider earning an IBM Data Science Professional Certificate. You can build a portfolio to showcase your work and practice using in-demand skills like SQL, Python, and machine learning. 

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 of high quality to maximize its usability. 

High-quality data has specific characteristics, such as completeness, relevance, and accuracy. Data engineers also use the extract, transform, load (ETL) 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.

Can a data engineer be a data scientist?

Since data engineers and data scientists share so many of the same skills, it’s possible to transition over to a data science career as a data engineer, or vice versa. To ease this transition, you could focus on gaining data analysis skills, as data scientists generally spend more time analyzing data and less time managing database architecture.

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.

Education requirements for a data engineer vs. data scientist 

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, boot camps, and certifications. 

You can also pursue valuable Specializations and Professional Certificates, such as a Google Cloud Database Engineer Specialization or Google Advanced 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.

Who gets paid more, a data scientist or a data engineer?

According to Glassdoor, the estimated median total pay for data engineers in the US is $131,000 [1], while data scientists can earn $153,000 [2]. This figure includes base salary and additional pay, which may represent profit-sharing, commissions, bonuses, or other compensation. Along with their high pay, data-related positions have a strong outlook over the coming years. The US Bureau of Labor Statistics (BLS) projects a 34 percent growth in data science jobs from 2024 to 2034 [3] and a 4 percent growth for database architects, who share similar responsibilities to data engineers [4].

Explore our free resources for data engineers and data scientists

Interested in becoming a data engineer or data scientist? Check out some of our free resources, like our LinkedIn newsletter, Career Chat, to stay in the know with the latest developments.

• Watch on YouTube: Career Spotlight: Data Engineer

• Test your skills: Python Skills Assessment

• Hear from an industry expert: 6 Questions with an IBM Data Scientist and AI Engineer 

If you want to develop a new skill, get comfortable with an in-demand technology, or advance your abilities, you can keep growing with a Coursera Plus subscription. You’ll get access to over 10,000 flexible courses. 

Build job-ready skills with Coursera Plus

Start 7-day free trial

• 
• 
• 
• 

Start 7-day free trial