Find a Job You Really Want In
As companies put an even greater focus on generating, collecting, and analyzing data as a way of streamlining and improving their business, jobs like Data Engineers and Data Scientists will continue to be in high demand. In fact, these types of jobs are expected to grow by at least 22% between 2020-2030.
Therefore, if you’re a current or prospective data science worker, you might be wondering what the difference between a Data Scientist and Data Engineer is. After all, even though the positions are similar, they have different education requirements and allow you to perform different tasks.
For example, while a Data Engineer might focus on building and maintaining data, a Data Scientist would instead spend most of their time analyzing and improving data. Of course, there are other vital differences as well.
Overall, to find out more about all of the important differences between Data Engineers and Data Scientists, this article will dive into everything you need to know.
What Is a Data Engineer?
Data Engineers are IT professionals who specialize in preparing data for analytical or operational use. Often, this job entails designing, building, testing, integrating, managing, and optimizing data until a fleshed-out infrastructure is produced. Some of the most common raw data elements Data Engineers work with include formatting, resilience, scaling, data storage, and security.
Here are the three main roles a Data Engineer can take on outlined:
-
Generalist: These professionals will often work in small teams with other Data Engineers, performing tasks like end-to-end data collection, intake, and processing.
Usually, generalists may be new to the career or working with smaller companies. Therefore, if you’re looking to transition from one data science position to another, a generalist role could be a good fit for you.
-
Pipeline-Centric Engineers: These specialists work with larger data analytics teams and work with midsize to larger companies. Some of the more complicated projects worked on by pipeline-centric engineers could include something like creating a tool for data scientists to search metadata about crucial company information.
-
Database-Centric Engineers: Typically, the role available at the largest companies, these professionals are tasked with building, maintaining, and populating analytics databases.
The complexity of the task will depend on how many databases and other teams the Data Engineer needs to work with. Overall, database-centric engineers will work with pipelines, tune databases, and create table schemas with extract, transform, and load methods (ETL).
All of these tasks and roles require Data Engineers to understand software and programming; however, the complexity of said skills will depend on the size and goals of the employer’s company. Overall though, one of a Data Engineer’s primary goals is to make the data they build for their company accessible, easily optimizable, and functional.
What Requirements Do Data Engineers Have?
Because programming and software engineering are integral to a Data Engineer’s role, you’ll need the proper education and certifications to start your career.
Typically, a Data Engineer will start their career path by pursuing a Bachelor’s Degree. Common relevant degrees include applied mathematics, computer science, physics, or engineering. While some prospective Data Engineers may choose to continue with a Master’s Degree, this isn’t necessary to achieve entry-level work.
Next, many prospective Data Engineers can benefit from taking specific online courses. These courses can help hone difficult programming and software-related skills. Specific examples might include online programs that help you navigate Python and other programming languages.
Additionally, to appear more professional to companies, a Data Engineer may opt to achieve certain certifications as well. Some of the most common certificates companies like to see are Google’s Professional Data Engineer or IBM Certified Data Engineer.
What Is a Data Scientist?
While Data Scientists work within many of the same systems as Data Engineers, their main role succeeds that of the latter. In other words, these professionals analyze and pull new insights out of the data built and managed by Data Engineers. This analysis can include conducting online experiments, developing hypotheses, identifying trends, and creating forecasts for the business.
Ultimately, these discoveries will allow a Data Scientist to draw important conclusions about a company’s current state. And in turn, these conclusions can be used to create forecasts for the business that can be formally presented to other branches of the company.
Overall, common duties of a Data Scientist include:
-
Collecting big data and converting it into a more digestible format.
-
Using data-driven techniques to solve business problems.
-
Working with programming languages (Python C#, Java, R, etc.)
-
Understanding and implementing machine learning, deep learning, and text analytics.
-
Communicating and collaborating with other IT workers and company employees.
-
Finding patterns and trends in big data that are relevant to a company’s needs.
What Requirements Do Data Scientists Have?
Programming and software engineering are also vital for the role of a Data Scientist, with the added importance of dissecting patterns and trends that provide company insights. With that in mind, education is vital for honing these skills.
First, a Data Scientist will start their career path by pursuing a Bachelor’s Degree. Common relevant degrees include statistics, computer science, information technologies, mathematics, or data science.
In many cases, you’ll be able to land an entry job as a Data Scientist with any of these degrees, but most Data Scientists will continue their education until they earn a Master’s Degree or a Ph.D.
For Master’s degrees, we recommend pursuing the same subjects as those mentioned for the Bachelor’s degree. However, you may also have access to specific STEM Master’s degrees like biotechnology, engineering, and physics.
Of course, prospective Data Scientists can benefit from taking the same online courses Data Engineers would take. Programs that can help you master programming languages are a great way to learn and grow in your field.
What Are Some Differences Between Data Engineers and Data Scientists
There are a few crucial differences between Data Engineers and Data Scientists that can help you determine which career you want to pursue. Here are those differences outlined:
-
Educational Background
In many ways, Data Engineers and Data Scientists can have similar backgrounds. However, there are some slight differences that separate the two.
While both might have experience with computer science, Data Scientists have a greater focus on operations research, as well as the statistical and business side of IT.
Overall, there’s no one Bachelor’s or Master’s degree earned by either of these professionals. However, the focus taken by either one when setting off on their career path typically varies based on the skills required by the job.
-
Salary and Job Outlook
Data Engineers and Data Scientists have different average salaries in the U.S., in part due to their skills and the average level of education. For example:
-
Data Engineer: The average Data Engineer in the U.S. earns $107,000 per year ($51.47 per hour). Data Engineers in the 90th percentile can earn up to $141,000, while Data Engineers in the 10th percentile might earn only $80,000.
Between 2018 and 2028, the career is expected to grow by at least 21% and produce 284,100 job opportunities nationwide.
-
Data Scientist: The average Data Scientist in the U.S. earns $102,000 per year ($49.39 per hour). Data Scientists in the 90th percentile can earn up to $142,000, while Data Scientists in the 10th percentile might earn only $74,000.
Between 2018 and 2028, the career is expected to grow by at least 16% and produce 5,200 job opportunities nationwide.
With those differences in mind, the biggest takeaways are that Data Engineers earn a slightly higher average salary, but a Data Scientist’s salary is more variable. Additionally, there will be over 50X more Data Engineer jobs available in the next ten years when compared to Data Scientist jobs.
-
-
Responsibilities
As mentioned, Data Engineers and Data Scientists serve different IT roles within a company. In a sense, Data Engineers are the first piece of the puzzle because they design, build, test, and maintain the big data structures that Data Scientists analyze.
For example, a company might hire a Data Engineer to design and build an important data set. The Data Engineer is then responsible for testing and maintaining this data.
Afterward, the same company might hire a Data Scientist to analyze the data. The Data Scientist will discern important patterns and trends, record them, and then use them to identify core issues or other information relevant to the company’s goals.
-
Languages, Tools & Software
Both Data Engineers and Data Scientists work with programming languages and other software tools, but usage can also vary between the two careers.
Of course, much of this can depend on the employer, but it is common for Data Engineers to work with tools such as SAP, Oracle, Cassandra, MySQL, Redis, Riak, PostgreSQL, MongoDB, neo4j, Hive, and Sqoop. These tools are particularly useful for building, testing, or maintaining software, making them less common among Data Scientists.
On the other hand, Data Scientists rely even more heavily on programming languages. Python and R are the most commonly used, but there are many others that might favor them as well. Overall though, Data Scientists will combine the use of these programming languages with useful packages like ggplot2 Scikit-Learn, NumPy, Matplotlib, Statsmodels, and more to aid in their visualization process.
Data Engineer vs. Data Scientist FAQ
-
Which pays more, data engineer or data scientist?
A job as a Data Engineer pays 5% more on average. Data Engineers earn slightly more per year on average, especially on the lower end of earners. The bottom 10% of Data Engineers earn an average of $80,000 annually, while the bottom 10% of Data Scientists earn $74,000 annually.
However, the top 10% of Data Scientists earn slightly more on average than Data Engineers, and overall, the pay of both jobs is extremely similar.
-
Which is easier, data engineer or data scientist?
Neither working as a Data Engineer nor Data Scientist is easier. Both are careers heavily focused on programming and other aspects of software, making them difficult careers for anyone who struggles with mathematics, coding, and pattern recognition.
Though, it is worth noting that it can be challenging for a Data Scientist to change their career to a Data Engineer. This is because Data Engineering is more heavily focused on building data, which requires extensive knowledge of programming languages and tools.
This is not to say that a Data Scientist is unfamiliar with those things, but simply that they might struggle to adapt to the new role.
Overall, both jobs serve different purposes, and while neither is inherently harder than the other, they also aren’t interchangeable.
-
Is data engineering an entry-level?
Data Engineering can be entry-level or for those ready for a more advanced career. Generally speaking, entry-level data engineers will need a Bachelor’s Degree in a relevant major and then be able to start working as a generalist. This job will entail working with a team of other Data Engineers to help a company (usually smaller) build and manage its data.
With more experience and advanced education, many Data Engineers can then go on to work for larger companies as Pipeline-Centric Engineers, or Database-Centric Engineers, which are more complex roles.
Final Thoughts
While Data Engineers and Data Scientists both play an integral role in helping companies with big data, they are different professionals with different specialties.
In general, Data Engineers are focused on designing, building, testing, and maintaining the data itself, while Data Scientists use the data that already exists to draw important conclusions about a company’s current status. Data Engineering aims to create and optimize, while Data science aims to analyze and draw important information.
Overall, you can have a successful IT career with a company whether you pursue a career as a Data Engineer or Data Scientist. Just keep in mind that the education requirements, salary, and daily tasks will be different depending on the career you choose.
Author
Jack Flynn is a writer for Zippia. In his professional career he’s written over 100 research papers, articles and blog posts. Some of his most popular published works include his writing about economic terms and research into job classifications. Jack received his BS from Hampshire College.
