Data Science vs. Data Engineering - Which Career Is Best?

Leading organizations across industries are creating significant business advantages — from increased profits and improved processes to finding new strategic opportunities — from data. As businesses increasingly rely on data to uncover insights and optimize operations, the demand for skilled data professionals continues to grow. Career options are plentiful and diverse for data scientists and data engineers, with each playing a vital role in leveraging data to transform organizational outcomes.

But which of these career paths most suits you? This blog will explore the roles of data scientists and engineers, comparing responsibilities, skills, and salary prospects to help you make an informed career choice in the expanding realm of data science and engineering.

Understanding the Data Scientist Role

Data scientists play a crucial role in business and technology, acting as the bridge between complex data and actionable insights. They use advanced analytical methods to solve intricate business problems, helping organizations make data-driven decisions to drive growth and innovation.

Daily Life of a Data Scientist

Collaboration and analysis are daily activities for data scientists. They work closely with domain experts, such as sales and marketing teams, to identify the right data sources for specific business goals. They develop and test hypotheses using sophisticated statistical and machine learning techniques to uncover patterns and trends within large datasets.¹

Data scientists also focus on creating predictive models to forecast outcomes, and translate complex data findings into actionable insights through visualizations and presentations, making complex data accessible to non-technical stakeholders.²

Technologies Used by Data Scientists

These are some common tools data scientists use to create robust models and visualizations:³

• Python
• R
• SQL
• TensorFlow
• scikit-learn

These tools enable them to create robust models and visualizations, translating complex data into meaningful insights.

What is the Role of a Data Engineer?

Data engineers construct and maintain the infrastructure necessary for data analysis. They lay the foundation that enables data scientists to perform advanced analytics by ensuring data is accessible, reliable, and optimized for analysis.

Daily Life of a Data Engineer

Data engineers spend their days designing and maintaining robust data architectures. They create and manage data pipelines to transport data from diverse sources into centralized storage systems, such as cloud data warehouses. This process includes cleansing and organizing raw data to ensure it's in the optimal format for analysis.

They also collaborate with data scientists to ensure the data infrastructure supports the analytical and data visualization needs of the organization and implement quality control measures to maintain data integrity.^{2, 3}

Technologies Used by Data Engineers

A range of technologies help data engineers effectively manage data infrastructure:¹

• Hadoop
• Spark
• SQL
• AWS
• Azure

These tools help them construct efficient data warehouses and pipelines to enable seamless data integration and analysis.

While their work is closely related, data engineers and data scientists have distinct roles and focuses. Data engineers lay the groundwork by developing and maintaining data infrastructure, ensuring data is collected, stored, and accessible for analysis. Their work involves creating data pipelines and managing databases.

Conversely, data scientists analyze this data to extract insights, focusing on developing predictive models, interpreting data to guide business decisions and creating data visualizations to communicate the information.³

In terms of educational background, both roles often require degrees in areas like computer science or mathematics. Data engineers typically pursue studies in computer engineering or information systems, emphasizing courses in database management and software engineering.

Data scientists, however, may focus more on statistics or machine learning in their advanced studies. Career paths diverge, with data engineers often evolving into roles involving data architecture, while data scientists may advance to analytics leadership positions.

Technical skills also differ. Data engineers need proficiency in SQL and data warehousing technologies, while data scientists focus on statistical analysis and machine learning tools like Python and R.²

Data Science vs. Data Engineering Salary Comparison

According to Glassdoor, the median entry-level annual pay for data scientists is $150,000,⁴ while the median starting salary for data engineers is $109,000.⁵ These figures include base pay and additional compensation, reflecting the high demand for professionals skilled in data management and analysis across various industries.

Advancing to more senior and higher-paying positions in either field requires a commitment to continuous learning. Professionals who keep pace with evolving technologies, such as cloud computing and machine learning, stand out in the job market.

Additionally, developing business acumen and managerial skills can broaden your career prospects, enabling you to transition into leadership roles.

Career Progression for Data Engineers

For data engineers, the salary outlook improves significantly with experience.

With 4-6 years of experience, data engineers can expect a median total pay of $139,000.⁶

As they gain expertise, they can progress to roles like Lead Data Engineer, with salaries ranging from $167,000 to $248,000.⁶

Advanced positions such as Director of Data Engineering command salaries up to $280,161, and Senior Director roles earn $326,068, reflecting the strategic importance of data infrastructure in organizations.⁶

Climbing the Career Ladder As a Data Scientist

Similarly, data science professionals see substantial salary growth as they advance, highlighting the value of data-driven insights in strategic decision-making. With 4-6 years of experience, they can earn a median total pay of $179,000.⁷

Continued growth in expertise can lead to positions like Lead Data Scientist, with potential earnings between $207,000 and $320,000.⁷

Principal Data Scientists earn salaries up to $307,439, while Senior Director positions can reach $320,121.⁷

Making the Career Choice That's Right for You

Choosing between a career in data science vs. data engineering involves evaluating your personal strengths and interests.

If you enjoy building and maintaining systems, data engineering may suit your skills in infrastructure and pipeline creation.³

Alternatively, if you prefer analyzing data and developing models, data science offers opportunities to leverage statistical and machine learning expertise.³

Consider your long-term career goals and desired work environment. Data engineers often work on backend systems, while data scientists focus on data-driven insights for strategic decisions. Both paths offer rewarding prospects, with data science and data engineering roles being integral to modern business success.²

Chart Your Path to Success as a Data Scientist at New York Tech

Data science and data engineering are distinct but complementary paths, each essential to modern business. As a data scientist, you can drive innovation by turning data into actionable insights and strategic advantage.

Advance your career with New York Institute of Technology’s Online Data Science, M.S. program, which offers a flexible learning environment, personalized mentorship, and access to a network of industry experts. Begin your journey today by contacting an admissions outreach advisor for more information or starting your application now.