Data Engineer vs. Data Scientist: What Is the Difference?

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A data engineer analyzes code on a pair of monitors.

Most people think of data as a synonym for information. However, data scientists and data engineers view data as actionable intelligence, providing key clues and insights into a wide assortment of activities, behaviors, and characteristics.

While both data scientists and data engineers convert unorganized and unprocessed data into usable information, the two roles differ in notable ways. Individuals considering careers in the data industry can benefit from comparing the roles of a data engineer vs. a data scientist and understanding their distinct responsibilities, salaries, and job prospects.

Pursuing an online Master of Science in Engineering with a specialization in data science can help prepare aspiring data professionals to excel in either role.

Data Engineer vs. Data Scientist: Comparing Roles

Data engineers and data scientists often work together, frequently on the same projects, so their duties and responsibilities sometimes overlap. For example, both data scientists and data engineers write code when working on or developing computer software. Despite such similarities, each role has a unique focus.

What Does a Data Engineer Do?

Data engineering involves the development of equipment, architectures, and systems that make acquiring data possible. Data engineers design these components, enabling organizations to parse and analyze the data they’ve gathered. Without data engineers, the analysis of data to uncover meaningful insights, known as data science, would have no starting point.

However, data engineers also frequently deal with data sets to search for trends and patterns. This identification can inform how data engineers go about creating algorithms that make raw data — unorganized, unprocessed information — easier to interpret.

Other data engineer responsibilities include the following:

  • Using and translating data programming languages
  • Organizing and preparing data for modeling, both prescriptive and predictive
  • Coordinating system architecture in line with a client’s needs or requirements
  • Actively looking for ways to make data more unimpeachable, efficient, and of high quality
  • Deploying machine learning and statistical methods to optimize business or consumer processes

What Does a Data Scientist Do?

Whereas data engineers design the systems for data collection, data scientists handle the interpretation. Data by its very nature is massive, especially as society has grown increasingly digitized. In its raw form, it’s essentially words, numbers, or symbols on a page. Data scientists draw from their years of experience to make sense of data sets.

Sometimes, this process may be as straightforward as objectively assessing the data. In other situations, it may entail developing hypotheses based on what the data suggests. Predictive modeling, advanced analytics, and machine learning may be leveraged in these efforts — the architecture made possible by data engineers.

Additional data scientists are responsible for the following:

  • Developing or refining statistical learning models for more efficient data analysis
  • Assisting with predictive modeling processes
  • Consulting with other members of an engineering team, such as software engineers, mechanical engineers, or computer scientists
  • Communicating findings to project stakeholders
  • Corroborating and authenticating data to ensure accuracy and uniformity
  • Mining stores of big data sets
  • Cleaning and validating data to enhance its reliability

Data Engineer vs. Data Scientist Career Outlooks

The U.S. Bureau of Labor Statistics (BLS) tracks job growth information for data scientists but not for data engineers. Nonetheless, the hand-in-hand nature of the two roles suggests that the growth outlook for data engineers will likely track with that of data scientists.

Just as the economy affects other professions, it can affect the availability of job openings in data science. However, data engineers and data scientists work in many different industries, creating a variety of opportunities. The BLS expects jobs in data science to grow by an impressive 36% between 2021 and 2031 — seven times faster than the average job growth rate for all occupations — creating roughly 40,000 new jobs.

According to the BLS, data science professionals find the majority of their jobs in:

  • Computer systems design
  • Company and enterprise management
  • Technical consulting
  • Scientific research
  • Credit mediation

At the state level, California has more jobs in data science than any other, likely due to the state’s large population and the fact that it’s home to Silicon Valley. Other states with a large volume of professionals in this field include New York, Texas, North Carolina, and Illinois, based on the most recent BLS data.

At the same time, data science is a highly competitive field. Each year, job listings website Glassdoor releases its report detailing the “50 Best Jobs in America.” For the past seven years in a row — 2016 to 2022 — data scientist has topped the list. This is largely due to several factors, including what professionals typically earn in terms of salary.

Data Engineer vs. Data Scientist: Salary

Engineering is almost uniformly a high-paying profession, but data scientists and data engineers are among the better compensated. Payscale data from January 2023 shows that data engineers made a median annual salary of approximately $94,300, with the top 10% earning a median of more than $134,000.

Data scientists make a great living as well, earning a median annual salary of approximately $100,900 in 2021, according to the BLS, with the top 10% earning more than $167,000.

Location can affect data scientist and data engineer compensation. Currently, jobs in Washington state offer the highest pay for data science and mathematical science occupations, followed by California, the District of Columbia, Massachusetts, and Maryland.

The metropolitan areas with the highest salaries for data science professionals include San Jose, California; the San Francisco Bay Area; Seattle, Washington; the District of Columbia metropolitan area; and the New York tristate area, according to the BLS.

Should I Become a Data Scientist or Data Engineer?

Deciding whether to become a data scientist or a data engineer? Consider your interests. People who enjoy constructing the systems that enable the collection and processing of data may be drawn to the role of data engineer. On the other hand, those more partial to the analytical side of data collection may find the role of data scientist more well suited to their abilities and preferences.

Whatever you decide, an educational foundation in data factors greatly into a successful career launch. Discover how an online Master of Science in Engineering degree from the University of California, Riverside can equip graduates with the skills needed to thrive in a range of data professions. Take the next step toward advancing your career today.

Recommended Readings

Machine Learning vs. Deep Learning: How They Compare and What an MSE Can Teach You About Them

Social Media Key Performance Indicators That Data Scientists Should Know About

What’s the Difference Between Data Visualization and Data Analytics?



Amazon, What Is Data Science?

CIO, “What Is a Data Engineer? An Analytics Role in High Demand”

Glassdoor, 50 Best Jobs in America for 2022

Payscale, Average Data Engineer Salary

TechTarget, “Data Scientist”

U.S. Bureau of Labor Statistics, Computer and Mathematical Occupations

U.S. Bureau of Labor Statistics, Data Scientists

U.S. Bureau of Labor Statistics, Occupational Employment and Wages, May 2021, 15-2051 Data Scientists