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

Being a data engineer vs. data scientist means choosing between focusing on the construction of data storage solutions or on the analysis of data itself. While a career in data engineering involves primarily technical skills, like coding and understanding data warehouse architectures, data science requires statistical analysis and business intelligence skills.

What Does a Data Engineer Do?

The primary goal of data engineers is to build, maintain, and monitor data storage systems and pipelines. The simplest way to think about a data engineer’s job is to imagine making a user profile on a website. Filling out your information on the site is the “capture point” for data — like your name, email address, and phone number. That data needs to be stored somewhere, so engineers build a pipeline to bring the data from that capture point to a storage place, such as a data warehouse or data lake. 

If it’s a busy website, there will be a lot of data in storage. It needs to be sorted so that other people, like data scientists and analysts, can easily look at it and find information. So, data engineers also build transformation systems that convert messy, raw data into usable details and the pipeline that brings the data through the system. 

Data engineers consistently monitor it all to ensure it works the way it needs to. The data then goes on to be used by data scientists. 

“The data engineer does the groundwork preparing reliable data sources to help the data scientist provide accurate analytical outcomes,” says Dushyant Sengar, director of data science at BDO USA. 

What Does a Data Scientist Do?

Data scientists take the data that engineers have stored and find ways to use it in practical applications. 

“We look for the ‘signal in the noise’ by using methodological sound and meticulously planned steps, including parsing raw data to gleam the nugget of information contained within,” says Daryl Boykin, VP of analytics at Cane Bay Partners.   

There are many ways companies and organizations use data, and data scientists execute a variety of methods to help businesses make data-driven decisions. 

“This could be using statistical models to predict likelihood of payment defaults of loans, to determine if someone is cheating while playing in a casino, or if reviews are fake to bolster the online reputation of a product,” notes Boykin.

As the desire for data-driven decision-making grows in practically every industry, the need for data scientists (and engineers) will also increase. 

“Data science provides a way of taking advantage of this data and helps [companies] gain an edge over the competition,” adds Aaron Pickering, data scientist at FMC and co-founder of Seenly.io.

Data Engineer vs. Data Scientist Salaries

The main factors determining salaries for data scientists and engineers are location, experience level, industry, and employer. For example, tech giants like Meta and IBM may be able to offer higher salaries than small tech start-ups. Additionally, industries with more regulated or confidential data, like credit card information and patient medical records, may also pay more because of the inherent risk if data is not handled properly. 

According to the U.S. Bureau of Labor Statistics (BLS), the average annual salary for data scientists is $115,240. Estimates from Payscale are slightly more conservative, with an average salary of around $99,300. Indeed gives a higher estimation, with data scientist’s typical base pay being $123,600. 

Unfortunately, the BLS does not provide a salary breakdown for data engineers, though estimates from Indeed suggest data engineers could make an average base salary of around $124,600. Payscale gives a range for data engineer salaries from $67,000 to $135,000. Those early in their careers would likely see the lower end of the scale, while more experienced engineers may be able to exceed the higher end.

Average Pay Estimates

Experience Level	Data Engineer	Data Scientist
Early Career (<1 Year Experience)	$95,500	$119,900
Average for All Experience Levels	$115,400	$152,200
Experienced (>15 Years Experience)	$158,800	$195,000

It’s important to remember that both data engineers and data scientists may see additional compensation in annual performance bonuses and stock shares. Additionally, this salary information is specific to the United States, so salaries for roles in other countries or at companies based internationally may differ.

Becoming a Data Scientist vs. Data Engineer

Education and Background

Both careers can benefit from a degree in computer science, information technology (IT), or applied mathematics. However, there are some key differences in the types of additional coursework students should take for each career. 

Data Engineer-Specific Education 

Students interested in pursuing data engineering should prioritize technical skills. 

“Data engineering requires mostly programming and data manipulation understanding,” says Sengar.

While experience in data analytics and statistics can be helpful down the line for data engineers who want to transition into more analytical roles, these competencies aren’t necessary for most data engineering careers. Sengar suggests moving into data engineering is easier than moving into data science because the skills are primarily technical rather than analytical. 

Data Scientist-Specific Education

While prospective data scientists can benefit from computer science, IT, and applied mathematics degrees, some schools may also offer degrees in analytics. However, students need to ensure they still learn the core technical skills necessary for data science: coding, machine learning, and building data infrastructure. 

“Many colleges and universities are now offering certificates or minors in data science and analytics, which can give you a good start,” says Tanya Cofer, senior risk analytics manager at Cane Bay Partners.

Students can diversify their skill sets by minoring or getting a certificate in data science. For example, a degree in economics with a minor in data science could boost a student’s resume if they’re looking for data science positions with the U.S. Treasury Department. 

Internships

Internships can expose students to real-world projects, boosting their understanding of how and why data science and engineering work in various industries. 

“If you get the chance to do an internship in analytics, do not hesitate. Take it,” says Cofer to prospective data scientists. “The experience is something you can put on your resume, and it will go a long way towards building your credibility.”

Additionally, regardless of field, internships can be a networking opportunity, making landing a role after graduation easier. 

Certifications and Bootcamps

Both careers have similar offerings in terms of certifications, bootcamps, and internships. Many big tech companies offer certifications and programs that can prove your skills in specific areas of data science and engineering. For example, IBM has a certificate for data engineers focusing on big data and a separate course for data scientists that covers SQL, Python, and machine learning. 

Online bootcamps are similar — there are options for either career path and even ways to use them to gain a specialization in areas like machine learning, big data, and business intelligence. 

These certifications and bootcamps can also be great opportunities for people to transition into data engineering or science from a different job. 

“I know many people who have also switched from other careers to the field after taking courses to bridge the knowledge gap,” says Pickering.

>>MORE: See Forage’s picks for the best online coding bootcamps for 2023.

Advancement Opportunities

Many people get into data engineering later in their careers, typically starting as data scientists or software engineers. Data engineers can progress to become lead architects, managing a team of data engineers. 

However, data engineers can also stick to the same role and grow in responsibility, project size, and specialization — for example, moving from a role as a data engineer on a team of dozens of engineers to a position as a lead engineer for a large-scale company or project. 

On the other hand, data scientists usually begin their careers as analysts after graduation or transitioning into the career. 

“We steadily gain more responsibility, take on more sensitive and critical projects, and we may become leaders of teams of analysts,” says Cofer.

Like in data engineering, there is plenty of room for growth and ways to take on leadership roles. 

“Some data scientists can choose to move into a management role, mentoring and guiding a team of analysts while some prefer to continue working as an independent contributor,” adds Boykin. 

Required Skills for Data Engineering vs. Data Science

Data Engineering Skills

Despite being highly technical, data engineers rely heavily on certain soft skills to do their jobs effectively. 

According to Sengar, “they need to interface a lot with other business teams and data users such as data scientists.” 

Sengar explains that data engineers also need soft skills like:

• A curious mind
• Problem-solving skills
• Communication with tech and non-tech audiences

As for hard skills, data engineers need to “understand various data storage architectures and know medium to advanced SQL to query these storage architectures,” says Sengar.

But data engineers should also be familiar with:

• Data warehouse platforms like Amazon’s Redshift and IBM’s Db2 Warehouse
• Cloud computing
• Operating systems like Microsoft Windows and Linux
• Programming languages like Python, JavaScript, and Scala 

Data Science Skills

The work data scientists do is at the “intersection of statistics, coding, and business knowledge,” according to Cofer, so data scientists need a strong mix of hard and soft skills to succeed. 

Some of the most crucial soft skills for working in data science are: 

• Communication
• Analytical thinking
• Problem-solving

“Something I’ve noticed about the most successful data scientists is flexibility, confidence, and competence when it comes to coding languages,” adds Cofer. 

But flexibility, in general, is a vital skill in data science — data is constantly growing and changing, as well as the industries and businesses data scientists work in. Taking these changes in stride can set a data scientist apart from the competition. 

For more technical or hard skills, the key skill for data scientists is statistics. According to Cofer, data scientists “use statistics every day, whether reporting simple summary statistics or checking statistical requirements for machine learning models used for making business decisions.” 

Data scientists should also have hard skills like:

• Data analysis
• Programming languages like Python, R, and SQL 
• Machine learning
• Data and analysis ethics, including biases, privacy, and security

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Bottom Line: What’s the Difference?

Data engineers and data scientists are just two parts of the puzzle that seeks to solve the problem of data: where do we get it, and how do we use it? There are many crossovers between both career paths — some data scientists leave the statistics and analysis behind and move into data engineering to focus on the data pipeline. 

On the other hand, some data engineers get curious about what happens to the data they have stored, and they seek out opportunities to learn more applied mathematics to go into data science. 

But, the key difference is: 

• Data engineers build
• Data scientists study

	Data Engineer	Data Scientist
Primary Goal	Build data storage solutions and pipelines to carry information from extraction through transformation processes	Analyze data to find patterns and insights to inform business decisions
Average Entry-Level Salary	$95,500	$119,900
Education and Background	Degree in computer science, IT, or other tech-focused field, as well as certifications and bootcamps to prove higher-level skills in specialized areas	Degree in computer science, IT, statistics, math, analytics, or other tech field, as well as certifications and bootcamps to prove higher-level skills in specialized areas
Top Soft Skills	Communication Curiosity Problem-solving	Communication Analytical thinking Problem-solving
Top Hard Skills	SQL Data storage architectures Data warehouse platforms	Statistics Python Data analysis
	Learn more	Learn more

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