Data Scientist vs. Data Engineer

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

If you’re considering a career in data science, now is a great time to get started. The Bureau of Labor Statistics estimates that positions for data scientists will increase by 16 percent between 2018 and 2028 ⁠— a rate more than three times that of the average growth expected for all other occupations. Furthermore, data positions such as data scientist and data engineer have topped the list of Glassdoor’s annual rankings for the 50 best jobs in America.

When looking over the various job postings for data scientists and data engineers, you will likely discover overlapping information surrounding experience, skills, and education. While the roles may appear very similar, they differ quite a bit.

If you’re interested in either of these paths for your career, it’s important to gain a clear understanding of where you’ll be going and what you can expect to find when you get there.

What Is a Data Scientist?

Data scientists are responsible for collecting, managing, and extracting new information from large sets of data. They design the framework to house it and then deploy their analytical skills to gain a better understanding of markets, consumer bases, and business needs.

These professionals are a new breed of analytical data experts who have the technical skills to identify and solve complex problems — and the creativity to look at these problems in new ways. While the role of a data scientist may not traditionally be viewed as creative, it would be misguided to undervalue the crucial role of creativity in data science. As IBM describes, data scientists are “part analyst, part artist.”

If you were to break down the role of a data scientist into a single question, it would likely be: How can we use information to solve problems that we don’t even know we have yet? While the responsibilities that a data scientist takes on will vary across industries and employers, most in the profession share a few core responsibilities. These include but are not limited to:

  • Extracting and analyzing large amounts of information to identify trends and patterns
  • Proposing solutions and strategies to business challenges
  • Collaborating with various technical/functional stakeholders to implement models and monitor results
  • Developing processes and techniques to analyze and monitor model performance while ensuring data accuracy
  • Identifying valuable data sources and automating collection processes
  • These responsibilities have rendered data scientists invaluable in recent years. In fact, jobs in which data science skills are applicable are forecasted to be among the most in-demand roles across most industries by 2022, according to a report by the World Economic Forum.

Additional Resources:

What Type of Jobs Do Data Scientists Have?

The data science career path has rapidly become one of the hottest professions in the tech world. In 2019, LinkedIn reported that data science was the #1 most promising career path in the U.S., with a 56 percent year-over-year growth rate. You may be wondering what types of jobs these include.

Data science is a broad term that encompasses a variety of niche fields. Mathematics, machine learning, cluster analysis, data mining, Big Data analytics, artificial intelligence — if there’s a way to quantify something, there’s a good chance it has its own field.

The good news is that data scientists can anticipate a straightforward career advancement path, vaulting from an entry-level position to more senior roles. Here is an example of a general data scientist job path:

  • Junior Data Scientist: Zero to two years of experience. Typically works alone only on small and basic projects while collaborating with a team on more advanced projects.
  • Data Scientist: Two to five years of experience. Often manages small projects on their own, but typically does not lead big and/or advanced projects.
  • Senior Data Scientist: Five to ten years of experience. Typically owns and leads projects from end-to-end.
  • Principal Data Scientist: Ten or more years of experience. Expert in the field. Takes lead on advanced projects, especially those in new subject areas.

What Is a Data Engineer?

While data science and data scientists are more concerned with analyzing data, identifying trends and patterns, and developing processes, data engineers are more involved in ensuring that these algorithms work from a production standpoint. These professionals typically recommend and implement techniques to enhance data reliability, proficiency, and quality.

To do so, DataDiversity’s Keith D. Foote states: “Data engineers need to have a solid understanding of commonly used scripting languages and are expected to support the steady evolution of improved data quality, and increased quantity, by leveraging and improving data analytics systems.”

Here are several responsibilities that are common among data engineers in the U.S., according to CIO:

  • Utilize and translate data programming languages
  • Deliver updates to stakeholders based on analytics
  • Coordinate system architecture in line with a client’s needs or requirements
  • Deploy sophisticated analytics programs, machine learning, and statistical methods
  • Prepare data for predictive and prescriptive modeling

While responsibilities of data scientists and data engineers may occasionally overlap, the two positions are uniquely individual roles. Data scientists are often responsible for using data to discover new insight, while data engineers focus much more on building the foundation for infrastructure used in data generation. Essentially, data scientists need data engineers to develop the environment and infrastructure they work in.

Additional Resources:

What Are the Responsibilities of a Data Engineer? (Springboard)

A Beginner’s Guide to Data Science (KDnuggets)

Most Frequently Asked Questions About Data Engineering (TowardsDataScience)

What Type of Jobs Do Data Engineers Have?

Much like data scientists, data engineering roles are among the fastest-growing jobs in the U.S. according to recent figures. The Dice 2020 Tech Job Report labeled data engineer as the fastest-growing job in technology in 2019, with a 50 percent year-over-year growth in the number of open positions.

The same report also found it takes an average of 46 days to fill data engineering roles and predicted that the time to hire data engineers may increase “as more companies compete to find the talent they need to handle their sprawling data infrastructure.” It is clear that organizations need data engineers who can share insights and discover new value from their data. Here are some of the roles they are looking for:

  • Junior Data Engineer: Zero to two years of experience. Responsible for ensuring best practices are integrated within data pipelines.
  • Data Engineer: Two to five years of experience. Typically work cross-functionally with data scientists to understand their specific needs for a project.
  • Senior Data Engineer: Five to ten years of experience. Responsible for owning, managing, and finalizing all aspects of data development activities.
  • Lead Data Engineer: Ten or more years of experience. Responsible for developing and scaling high-performance data teams.

Understanding the Hierarchy of the Data Process:

The use of data today follows a pattern similar to Maslow’s hierarchy of needs. According to data scientist consultant Matthew Renze, many organizations are becoming stalled in their data science journey at some point in time. Sometimes it is because they have neglected to build the foundation needed to transition to the next level; other times, it’s because they’re unsure of where to go next, or how to get there.

This methodology is based on the premise that organizations behave like individuals when trying to achieve their goals. To help you understand how this framework applies to data, let’s begin with Maslow’s theory, as outlined by Simply Psychology:

  1. Physiological needs: These are biological requirements for human survival (e.g. air, food, drink, shelter, clothing, warmth, sex, and sleep).
  2. Safety needs: Once an individual’s physiological needs are satisfied, the needs for security and safety become salient. These needs can be fulfilled by family and society (e.g. police, schools, business, and medical care).
  3. Love and belonging needs: After physiological and safety needs have been fulfilled, the third level of human needs is social and involves feelings of belonging. The need for interpersonal relationships motivates behavior.
  4. Esteem needs: The fourth level in Maslow’s hierarchy, which Maslow classified into two categories: (i) esteem for oneself (dignity, achievement, mastery, independence); and (ii) the desire for reputation or respect from others (e.g., status, prestige).
  5. Self-actualization: The highest level in Maslow’s hierarchy, referring to the realization of a person’s potential, self-fulfillment, personal growth, and experiences. Maslow describes this level as the desire to accomplish everything that one can.

The best data-driven organizations grow and evolve over time, according to Renze. They go through various stages of growth as they reach maturity, which are based upon a hierarchy of data-driven needs. Essentially, organizations can’t proceed to the next stage of organizational transformation until they have sufficiently satisfied lower needs.

As we can see in Maslow’s model, every stage is a necessary foundation for the stage above it. If the foundation is not strong, higher stages may break down even if they initially work, according to Treasure Data. Resources for building each stage involve a combination of human talent and the right set of tools.

Here is a visual example to help you better understand how data in an organization follows a pattern similar to Maslow’s model.

Data Science Hierarchy of Needs

Data Engineer vs. Data Scientist Salary: How Much Do They Earn?

Comparing data engineer and data scientist salaries is not black and white as both will vary based on specialties and experience. There will also be discrepancies in pay based on location, company, work status (part-time, full-time, freelancer, or contractor), team management style, remote versus in-person work, education, and time with a company or client.

Generally, comparing data engineer to data scientist earnings will typically show similar salaries. According to the U.S. Bureau of Labor Statistics, computer and information research professionals such as data engineers and data scientists typically earn a median annual wage of $122,840.

It is important to know that data engineer and data scientist salaries can vary greatly. The U.S. Bureau of Labor Statistics information represents national data, averaged for the occupations listed and includes workers at all levels of education and experience. This data does not represent starting salaries.

Final Thoughts:

Now that you have a better understanding of both of these roles, you can better decide which path is for you. The fields of data science and data engineering are diverse and growing fast. While data scientists and data engineers roles differ, there is unquestionable potential for both.

Companies today are competing for increasingly scarce data scientists and data engineers to come together and solve their most challenging data problems. If you’re interested in taking a dive into the data science career path, University of Arizona Data Analytics Boot Camp may be right for you.

Contact our admissions team at (520) 917-1930 to learn more about the various career possibilities you can pursue after completing our program.

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