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Data Scientists vs Data Engineers: What's the Difference?

Data engineers build the infrastructure that enables Data scientists to analyze data at scale and draw inferences. Learn about each role’s skills, jobs & education.

Data science vs Data engineering

With the demand for data professionals rapidly rising as big data becomes increasingly valuable to businesses, pursuing a career as a data scientist or data engineer is a promising career move. While the two professions are related, they have distinct differences in their responsibilities, skills, training and more.

While data engineers design, build and maintain the architecture to store, process and secure an organization's data sets so they're accessible to data scientists, data scientists develop statistical models to explore and analyze the data. To do this, data scientists use Python programming, machine learning, and other computer science techniques to discover hidden patterns, glean valuable insights and make predictions to aid businesses or organizations. Data engineers are crucial to data scientists, and data scientists are crucial to businesses that want to utilize their data to its full potential.

Understanding these and other differences between data scientists and data engineers can help inform your professional development to prepare for the career you want. This article will provide an in-depth discussion of the differences between the two booming fields.

Differences in Roles & Responsibilities

One of the main differences between a data engineer and a data scientist lies in their roles and how they work with data. A data engineer helps develop and implement a comprehensive architecture solution (often involving cloud services and relational databases) that store and process raw data in a secure manner, while a data scientist will then use the raw data to analyze and glean insights to give the organization a competitive advantage.

Data Engineer Roles & Responsibilities

Data engineers maintain one of the organization's most valuable resources--data--making it more accessible to data scientists. Their tasks often include:

  • Constructing, testing and maintaining databases, cloud storage services, and large-scale processing systems.
  • Merging systems so that system-specific data can be used.
  • Validating the data to identify and remove errors.
  • Recommending and implementing ways to improve data reliability, data security, processing efficiency and quality.
  • Developing processes for producing, mining, and modeling data sets.

Data Scientist Roles & Responsibilities

Data scientists, on the other hand, take that prepared data and use machine learning, statistical methods, and analytical tools to parse out valuable insights and make predictions. They then present that information to businesses and stakeholders. Their tasks may include:

  • Conducting research to discover industry or business questions/problems.
  • Working with large volumes of often-incomplete (or "dirty") data to find answers and solutions to complex questions or problems.
  • Examining data to discover hidden insights and patterns.
  • Utilizing advanced analytical, statistical and machine learning methods to prepare data for prescriptive and predictive modeling.
  • Automating processes so clients can continue to receive insights on a regular basis.
  • Using storytelling and data visualization to present the information to clients and stakeholders in an easy-to-understand, digestible format.

Learn more about what data scientists do in their day-to-day work.

Differences in Tools, Programming Languages & Software

Whether you decide to become a data engineer or data scientist, you will leverage common programming languages like Python, Java, and Scala. However, each occupation works with unique tools, programming languages, and software beyond those, and they can vary depending on the size of the organization and context of the data engineer or data scientist’s role within that organization.

Data engineers work primarily with database, data processing, and cloud storage tools, while data scientists use programming languages and tools for complex, statistical data analytics and data visualization. Below are a few examples of tools commonly used by each:

Data Engineering Tools

  • SAP
  • Amazon Web Services ("AWS")
  • Microsoft Azure
  • Oracle
  • Cassandra
  • PostgreSQL
  • Redis
  • MongoDB
  • Hive

Data Science Tools

  • Python
  • Matplotlib
  • R
  • SPSS
  • SAS
  • Tableau
  • RapidMiner
  • Gephi

Differences in Data Scientist and Data Engineer Education Backgrounds

An educational background in computer science and/or computer science skills are essential to both data engineers and data scientists. However, data engineers and data scientists often followed different career paths before arriving in their professions, and that’s where they differ.

Education Requirements for Data Engineers

Data engineers tend to come from IT and engineering backgrounds, perhaps with work experience as Database developers or Computer systems analysts. If you’re preparing for undergraduate study, consider degrees in computer science or engineering. To give yourself a competitive edge, earn program-specific certifications afterward, like the associate-level SAP certification for Master Data Governance.

Graduate programs can increase your competitiveness and make you eligible for higher pay and more advanced professional opportunities, as well as expand your expertise in your field. If you’re applying for graduate programs with the intention of furthering your career as a Data engineer, there are several beneficial areas of study to choose from.

Rice University offers a few relevant degrees, such as the Master of Computer Science (MCS) (both 100% online or on-campus), which emphasizes software systems and algorithms, databases and big data cloud storage solutions. For aspiring Data engineers who also want to engage in the Data science and analytics side, the Rice Master of Data Science (MDS) program (both 100% online or on-campus) will balance databases and big data cloud solutions with specializations in either Business Analytics or Machine Learning.

Education Requirements for Data Scientists

Data scientists often come from backgrounds like mathematics and statistics, research and analysis, or business and management consulting because their roles require numerous areas of interdisciplinary knowledge across multiple fields. Earning an undergraduate degree in computer systems and information sciences, mathematics, or statistics would lay a solid foundation for a career as an entry-level data scientist.

If you’re interested in master’s programs that will enhance your role as a data scientist, Rice offers several options. For "career" Data scientists, Leads or Chief Data Officers, the Professional Online Master of Data Science (MDS) program features a customizable curriculum with either a Business Analytics or a Machine Learning specialization, plus the D2K Lab (Data To Knowledge Lab) Capstone that provides hands-on experience addressing real-world problems. For Engineers who want to remain in their engineering technical area (like mechanical or biomedical) while learning big data and data science techniques and skills, Rice offers a Data science specialization within the Master of Engineering Management & Leadership degree.

While education highlights another difference between data science vs. data engineering, it’s important to note that different backgrounds are beneficial to developing careers in both areas. The professionals in data-related fields come from a variety of education and work histories, including research, engineering, geology, management consulting, data analysis, database administration, and much more.

Differences in Job Title & Salary

As you may have noted above, there are general differences in the roles that a data scientist or data engineer would take on. Data engineers typically work to source, store, process, and secure raw data assets within their organizations, designing and managing secure databases, cloud storage solutions and data pipeline formations. Data scientists’ roles are somewhat broader and include responsibilities like applying data insights to quant finance, operations, marketing and general business strategies.

Your salary will be influenced by your experience, location, responsibilities, and scope of work, but below are the current averages for job titles in each industry.

Data Scientist vs Data Engineer Job Titles & Salaries

DATA SCIENTIST JOB TITLES AVG ANNUAL SALARY
Lead Data Scientist $143,166
Data Scientist Manager $162,000
Big Data Analyst $103,757
DATA ENGINEER JOB TITLES AVG ANNUAL SALARY
Lead Data Engineer $166,437
Big Data Engineer $116,231
Database Administrator $91,385
*Source: BLS, Indeed.com, Glassdoor, Glassdoor

Differences in Data Engineer vs Data Scientist Job Outlook

In recent years, according to some sources, the Data engineer role has grown faster than its Data scientist counterpart, although both are among the fastest growing occupations relative to all occupations. According to the DICE Tech Jobs Report (Jan-June 2022), the cloud computing skill was the #1 tech skill by job postings growth, growing +162% year over year. Data engineering was another top skill, increasing +102% in job postings growth year over year.

According to other sources, interviews for Data engineers increased 40% year over year, while Data scientist interviews grew at a slower pace of 10%.

Still, demand for Data scientist and Business analytics roles is projected to grow at a faster-than-average rate of +36% between 2021-2031 according to the Bureau of Labor Statistics.

Can a Data Engineer Become a Data Scientist?

Yes, a Data engineer will most likely have some technical knowledge in software engineering, computer science and systems already, including programming experience. However, Data scientists must also be fluent in statistics and math, allowing them to develop statistical machine learning models to conduct sophisticated data analysis at scale.

Depending on their skills and backgrounds, Data engineers looking to make the switch (or expand their roles) to Data science should brush up on Python programming, statistics and math.

Getting Started as a Data Engineer or Data Scientist

Now that you know more about each field, you can make an informed decision about the profession you wish to study. If you’re purely interested in working with raw data and computing, consider data engineering. If you prefer a more diverse position that blends communication skills, data science, machine learning, problem-solving, and creativity, then you may be a perfect fit for a data scientist position.

Whether you’re interested in becoming a data scientist or data engineer, you’ll need to start by securing an undergraduate degree in a relevant STEM field like Computer Science, Statistics or Engineering. While continuing education will be a constant for most throughout their careers, after at least 1-2 years of work experience, professionals may start to consider whether a master's program is right for them. Rice's Master of Data Science was designed specifically for the needs of aspiring Data Scientists, whereas the Master of Computer Science curriculum would be a better fit for aspiring Data Engineers.

Data Science employment is projected to grow +36% between 2021-2031.
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