Data Engineer vs Software Engineer: Key Differences Explored

Amongst the 9.1 million people working in the tech industry, data engineers and software engineers play crucial roles. However, their jobs differ significantly in their scope, user base, and required skill sets. Understanding these nuances can help you choose the right career path.

Data engineers and software engineers build the technological foundations that power our digital world. However, their day-to-day tasks and areas of focus vary greatly. 

Data engineers are responsible for creating frameworks and infrastructures for efficient data processing, analysis, and storage. In contrast, software engineers develop and maintain software applications, websites, and other digital products.

By exploring the key differences, you can determine which path aligns better with your interests, skills, and long-term goals. This article examines the distinct responsibilities, educational requirements, and earning potential of these two roles, helping you make an informed decision about your future.

Understanding Engineering Roles

Over the next decade, tech jobs are projected to grow at roughly twice the rate of overall employment in the economy. When browsing for job openings in the tech and data science fields, you may come across various “engineer” roles. Understanding the differences between these roles is crucial in determining the right career path. 

Let’s explore the key engineering roles you’re likely to encounter:

Data Engineer

Data engineers are responsible for designing, building, and maintaining the infrastructure that powers data-driven applications. They focus on creating efficient data pipelines, ensuring data accuracy, and making accurate data available for data scientists and analysts to leverage.

Data engineers often collaborate with software engineers to integrate data systems into broader software applications.

Software Engineer

Software engineers are responsible for designing, developing, and maintaining software applications. They work on a wide range of projects, from building websites and mobile apps to developing complex enterprise-level software systems. 

Software engineers typically focus on software design, coding, testing, and deployment, ensuring that the final product meets the user’s needs.

Machine Learning or AI Engineer

Machine learning (ML) or artificial intelligence (AI) engineers specialize in developing and deploying advanced algorithms and models that enable machines to learn and make decisions autonomously. 

The employment of machine learning engineers is projected to grow by 22% between 2020 and 2030. They work closely with data scientists to design, train, and integrate ML/AI components into software systems, enabling intelligent automation and insights.

Systems Engineer

Systems engineers are responsible for designing, implementing, and maintaining complex technical systems, such as computer networks, telecommunications infrastructure, or industrial control systems. 

They focus on ensuring the reliability, efficiency, and security of these systems, often working across multiple domains and collaborating with various stakeholders.

Core Differences: Data Engineer vs Software Engineer

While both data engineers and software engineers work with technology, the core focus and responsibilities of these two roles differ significantly. 

Understanding these distinctions is crucial when exploring a career path in the tech industry.

Scope of Work

Data engineers are primarily responsible for building and maintaining the infrastructure that powers data-driven applications and analytics. They focus on creating efficient data pipelines, data warehouses, and data lakes to ensure accurate and accessible data for data scientists and analysts. 

In contrast, software engineers are primarily focused on designing, web development, and deploying software applications and systems that meet the needs of a specific user base.

User Base

Data engineers typically collaborate with data scientists, data analysts, and business stakeholders to ensure that the data they manage and maintain is suitable for analysis and decision-making. 

Software engineers, on the other hand, work closely with product managers, designers, and end-users to understand their requirements and build applications that solve specific problems.

Key Skills

Data engineers require a strong understanding of data modeling, database management, and distributed systems, as well as proficiency in programming languages like Python, SQL, and Scala. 

Software engineers, on the other hand, must have a solid grasp of software architecture, coding best practices, and agile development methodologies, as well as expertise in languages such as Java, C++, or JavaScript.

Collaboration

Data engineers often collaborate with data scientists and analysts to ensure the reliability, security, and scalability of the data infrastructure. They work to optimize data pipelines and ensure that data is accessible and usable for analytics and decision-making. 

Software engineers, on the other hand, collaborate with product managers, designers, and other stakeholders to translate business requirements into functional software applications.

Understanding the core differences between data engineers and software engineers is essential in determining the right career path for your interests and skills. 

By exploring the scope of work, user base, key skills, collaboration, and popular tools for each role, you can make an informed decision about the direction that best aligns with your professional goals.

Aspect	Data Engineer	Software Engineer
Scope of Work	Build and maintain data infrastructure, including pipelines, warehouses, and lakes.	Design, develop, and deploy software applications and systems.
User Base	Collaborate with data scientists, analysts, and business stakeholders.	Work with product managers, designers, and end-users.
Key Skills	Expertise in data modeling, database management, distributed systems, Python, SQL, and Scala.	Proficiency in software architecture, coding practices, agile development, Java, C++, and JavaScript.
Collaboration	Ensure data reliability, security, and scalability; optimize data pipelines with data scientists and analysts.	Translate business requirements into functional applications with product managers and designers.

Comparing Salary Outlook

When it comes to the data engineer salary and software engineer salary, both career paths offer competitive earning potential. The average salary and median salary can vary depending on factors such as your location, experience level, and the industry you work in.

In the United States, the median salary for data engineers is around $128,037 per year, while software engineers can earn a median salary of around $105,287 annually.

However, these figures can fluctuate based on your geographic location. Data engineers and software engineers in tech hubs like Silicon Valley or New York City may earn higher salaries compared to those in smaller markets. 

Additionally, your level of experience can have a significant impact on your earning potential, with senior-level professionals typically commanding higher salaries.

Regardless of the specific numbers, both data engineer and software engineer roles are considered highly lucrative and in-demand within the technology industry. Your earning potential will depend on your unique qualifications, skill set, and the specific needs of the companies you choose to work for.

Role	Average Salary	Median Salary	Top Earning Locations
Data Engineer	$120,000	$130,000	San Francisco, New York City, Seattle
Software Engineer	$105,000	$115,000	San Francisco, Seattle, Boston

Education and Certification Requirements

The educational paths for data engineers and software engineers share many similarities, though there are key differences. Both career paths typically require a strong foundation in mathematics, computer science, and programming.

Degrees for Data Engineers

Data engineers often hold a bachelor’s degree in computer science, data science, or a related technical field. Many pursue master’s degrees in areas like data engineering, big data, or business analytics to deepen their expertise. Some may even have a doctorate, especially those focused on research or advanced data analysis techniques.

Data Engineer Certifications

Beyond formal education, data engineers often benefit from industry-recognized certifications such as:

• Certified Data Engineer from Cloudera
• AWS Certified Data Analytics Specialty
• Databricks Certified Data Engineer Associate

These certifications demonstrate proficiency in data modeling, data processing, and data pipeline design.

Degrees for Software Engineers

Similarly, software engineers commonly hold bachelor’s degrees in computer science, software engineering, or a related discipline. Many also earn master’s degrees in fields like software development, computer engineering, or information technology. 

While less common, some may have a doctoral degree in computer science or a specialized area of software engineering.

Software Engineer Certifications

Software engineers can enhance their credentials with certifications in programming languages, software frameworks, and development methodologies. Common certifications include:

• AWS Certified Solutions Architect
• Microsoft Certified: Azure Developer Associate
• Certified ScrumMaster from the Scrum Alliance

These certifications validate expertise in cloud computing, software design, and agile development.

Regardless of the specific educational path or certifications, both data engineers and software engineers must continuously update their skills to stay current with the rapidly evolving technology landscape. 

Ongoing professional development, whether through formal coursework, industry-specific training, or self-directed learning, is essential for success in these dynamic fields.

Essential Skills

While data engineers and software engineers share some overlapping skills, each role requires distinct competencies to succeed.

Data Engineer Skills

Data engineers focus on:

• Database management, data modeling, data warehousing, and data visualization
• Programming languages like Python, Scala, or Java
• SQL for data manipulation and querying
• Cloud computing platforms like AWS, Azure, or Google Cloud

They are adept at building efficient data pipelines, ensuring data accuracy, and collaborating with data scientists and analysts.

Software Engineer Skills

Software engineers focus on:

• Designing and developing software applications, systems, and tools
• Programming languages like Java, JavaScript, C++, or Python
• Software design patterns, algorithms, and data structures
• Software development methodologies like Agile
• Testing, debugging, and source control tools

By 2028, it is projected that 75% of enterprise software engineers will use AI code assistants. This shift underscores the need for software engineers to be adaptable and proficient in AI-related skills to stay competitive in the evolving tech landscape.

Strong problem-solving and collaborative skills are also essential, as they often work closely with cross-functional teams to deliver high-quality software solutions.

Understanding these educational requirements, certifications, and essential skills can help you decide which career path aligns best with your interests and strengths.

Conclusion

Understanding the differences between data engineers and software engineers is crucial for choosing the right career path. Data engineers focus on building data infrastructures, while software engineers concentrate on developing software applications. Both roles require distinct skill sets, educational backgrounds, and certifications.

If your organization needs expert guidance and development services to navigate these complex fields, consider partnering with Flatirons. 

Our custom enterprise web development services can help you build scalable and efficient systems tailored to your specific needs.