Data Engineers Vs. Data Scientists: Understanding The Difference

As businesses increasingly prioritize data generation, collection, and analysis to enhance operations and decision-making, the demand for professionals in roles such as Data Engineers and Data Scientists is projected to remain high. Job growth in these fields is anticipated to continue rising, with estimates indicating an increase of around 22% through 2030.

If you are currently working in or are considering a career in data science, you may be curious about the distinctions between a Data Scientist and a Data Engineer. While these roles share similarities, they differ in their responsibilities, skill sets, and educational backgrounds.

For instance, a Data Engineer typically focuses on constructing and maintaining data systems, whereas a Data Scientist dedicates their time to analyzing and interpreting that data. This article will explore the essential differences between Data Engineers and Data Scientists, providing insights to help you navigate your career path.

What Is a Data Engineer?

Data Engineers are IT specialists who concentrate on preparing data for analytical and operational use. Their work involves designing, building, testing, integrating, managing, and optimizing data infrastructures. Common elements that Data Engineers handle include data formatting, resilience, scalability, storage, and security.

Data Engineers can take on various roles, including:

• Generalist: Generally found in smaller teams or companies, generalists handle end-to-end data collection and processing tasks. This role is suitable for those new to the field or seeking to transition within data science.

• Pipeline-Centric Engineers: These professionals collaborate with larger data analytics teams in mid-sized to large organizations, often working on complex projects such as creating tools for data scientists to access metadata about critical company information.

• Database-Centric Engineers: Typically found in large corporations, these engineers are responsible for constructing, maintaining, and populating analytics databases. Their work involves tuning databases and optimizing data pipelines using extract, transform, and load (ETL) methodologies.

Regardless of their specific role, Data Engineers must possess a solid understanding of software and programming, with the complexity of these skills varying based on the employer’s size and objectives. Ultimately, one of the primary goals of a Data Engineer is to ensure that the data infrastructure they create is accessible, optimizable, and functional.

What Requirements Do Data Engineers Have?

Given the importance of programming and software engineering in a Data Engineer’s role, obtaining the right education and certifications is crucial to starting a successful career.

A typical career path for a Data Engineer begins with a Bachelor’s Degree in relevant fields such as applied mathematics, computer science, physics, or engineering. While some may pursue a Master’s Degree, it is not essential for securing entry-level positions.

Many aspiring Data Engineers can also benefit from enrolling in specialized online courses that enhance their programming and software skills, particularly in languages like Python.

Additionally, obtaining certifications can enhance a Data Engineer’s professional appeal. Notable certifications include Google’s Professional Data Engineer and IBM Certified Data Engineer.

What Is a Data Scientist?

Data Scientists operate within many of the same systems as Data Engineers, but their role transcends that of the latter. They analyze and derive insights from the data curated by Data Engineers, conducting experiments, developing hypotheses, identifying trends, and generating forecasts for businesses.

These insights enable Data Scientists to draw significant conclusions about a company’s performance, which can be communicated to various departments within the organization.

Common responsibilities of a Data Scientist include:

• Collecting large datasets and transforming them into more manageable formats.

• Applying data-driven methodologies to address business challenges.

• Utilizing programming languages such as Python, C#, Java, and R.

• Implementing machine learning, deep learning, and text analytics techniques.

• Collaborating with IT teams and other business units.

• Identifying relevant patterns and trends within large datasets to inform business strategies.

What Requirements Do Data Scientists Have?

For Data Scientists, programming and software engineering skills are essential, with an added emphasis on analyzing trends and patterns that provide business insights. Thus, educational qualifications play a critical role in developing these competencies.

A typical Data Scientist career path starts with a Bachelor’s Degree in fields such as statistics, computer science, information technology, mathematics, or data science. While entry-level positions may be attainable with these degrees, many Data Scientists further their education by obtaining a Master’s Degree or Ph.D.

For graduate studies, candidates are encouraged to pursue degrees in the same areas as their undergraduate studies, with potential access to specialized STEM Master’s programs like biotechnology, engineering, and physics.

Prospective Data Scientists can also benefit from taking online courses similar to those recommended for Data Engineers, focusing on programming languages and data analysis techniques.

What Are Some Differences Between Data Engineers and Data Scientists?

Understanding the differences between Data Engineers and Data Scientists can help you determine which career path aligns with your interests. Here are the key distinctions:

1. Educational Background

   While Data Engineers and Data Scientists often share similar educational backgrounds, they differ in focus. Data Scientists typically have a stronger emphasis on operations research, statistics, and the business aspects of IT. While there is no singular degree for either profession, their educational paths often reflect the specific skills required for their respective roles.

2. Salary and Job Outlook

   Data Engineers and Data Scientists command different average salaries in the U.S., influenced by their skills and educational levels. For example:

   • Data Engineer: The average annual salary for Data Engineers in the U.S. is approximately $107,000 ($51.47 per hour). Those in the 90th percentile can earn up to $141,000, while those in the 10th percentile can expect around $80,000. The field is projected to grow by about 21% from 2018 to 2028, creating approximately 284,100 job opportunities.

   • Data Scientist: The average annual salary for Data Scientists in the U.S. is around $102,000 ($49.39 per hour). The top 10% can earn about $142,000, while the bottom 10% earn around $74,000. The career is expected to experience a growth rate of 16% between 2018 and 2028, resulting in about 5,200 new job openings.

   In summary, while Data Engineers earn slightly higher average salaries, Data Scientist salaries tend to be more variable. Additionally, there are significantly more job openings anticipated for Data Engineers compared to Data Scientists over the next decade.

3. Responsibilities

   Data Engineers and Data Scientists fulfill distinct roles within organizations. Data Engineers are responsible for designing, building, testing, and maintaining data structures, while Data Scientists analyze this data to derive actionable insights.

   For example, a company may hire a Data Engineer to create a critical dataset, which they will then test and maintain. Subsequently, a Data Scientist will utilize that dataset to identify significant patterns and trends that align with the company’s objectives.

4. Languages, Tools & Software

   Both Data Engineers and Data Scientists utilize programming languages and software tools, but their applications differ. Data Engineers commonly work with tools such as SAP, Oracle, Cassandra, MySQL, and MongoDB, focusing on building and maintaining software systems.

   Conversely, Data Scientists rely more on programming languages like Python and R, often incorporating libraries such as Scikit-Learn and Matplotlib for data visualization and analysis.

Data Engineer vs. Data Scientist FAQ

1. Which pays more, data engineer or data scientist?

   A job as a Data Engineer pays 5% more on average. Data Engineers generally earn slightly more, especially among lower earners. The bottom 10% of Data Engineers make around $80,000 annually, while the bottom 10% of Data Scientists earn approximately $74,000. However, the top 10% of Data Scientists tend to earn slightly more than their Data Engineer counterparts, resulting in closely comparable salaries overall.

2. Which is easier, data engineer or data scientist?

   Neither role is inherently easier than the other. Both careers demand strong programming skills and a solid understanding of mathematical concepts, making them challenging for individuals who struggle with these areas. Transitioning from Data Scientist to Data Engineer can be particularly difficult due to the latter’s emphasis on data construction, which requires extensive programming knowledge.

   While both professions serve unique functions within an organization, they are not interchangeable.

3. Is data engineering an entry-level?

   Data Engineering can encompass both entry-level and advanced career positions. Generally, entry-level Data Engineers require a Bachelor’s Degree in a relevant field and may start as generalists, collaborating with a team to help smaller companies develop and manage their data. With experience and further education, Data Engineers can advance to more complex roles in larger organizations.

Final Thoughts

Data Engineers and Data Scientists are both essential to leveraging big data in organizations, yet they possess distinct expertise and focus areas.

Data Engineers concentrate on the creation, construction, testing, and maintenance of data infrastructures, while Data Scientists utilize existing data to extract valuable insights about organizational performance. The roles of Data Engineering and Data Science are complementary, with the former focusing on data optimization and the latter on data analysis.

Regardless of the path you choose—whether as a Data Engineer or Data Scientist—you can build a successful IT career. However, it’s essential to understand the differing educational requirements, salary expectations, and daily responsibilities associated with each profession.

Author

Jack Flynn

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.