The four key things to know about the difference between a data engineer and a software engineer are:

  1. Data engineers and software engineers earn a comparable salary and hold similar knowledge, but have different roles and responsibilities in the workplace.
  2. Although some software engineers work with data infrastructure, their responsibilities are still distinct from data engineers. Whereas data engineers are more micro-focused, software engineers look at things from a macro perspective.
  3. The data engineer role is ideal for individuals with experience in machine learning, big data, and building data pipelines. 
  4. The title of “software engineer” is a catch-all term that may apply to backend engineers, build engineers, database engineers, full-stack engineers, and more. 

When it comes to the world of technology, there are many roles that share similar responsibilities, from data scientists to data architects. It's easy to become confused by all the seemingly minor differences between such titles on paper.

The titles of data engineer vs. software engineer are a particularly good example—and a particularly confounding one, as there are a number of areas where they overlap. To help you understand the difference between a data engineer and a software engineer, this article will offer a more detailed comparison of these two roles and their potential importance within your organization.

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What is the Role of a Data Engineer?

For a proper “data engineer vs. software engineer” comparison, you have to first understand the roles and responsibilities of each.

The primary goal of a data engineer is to set up and maintain your organization’s data infrastructure. Data engineers work with the systems and databases that store your business-critical information that enterprise applications depend on. This data infrastructure ranges from small relational databases for startups to petabyte-scale systems used by massive multinational firms. 

As part of this role, data engineers must take on a number of responsibilities, including designing, building, and implementing data-driven systems to guide your organization’s reporting and analytics. This includes developing processes for mining, acquiring, transforming, migrating, verifying, and modeling, and mining your enterprise data. In addition, if you leverage artificial intelligence and machine learning as part of your data infrastructure, data engineers may be responsible for building, training, deploying, and refining AI models that efficiently analyze vast quantities of information.

So what does a data engineer need to know in order to accomplish all of these tasks? Data engineers typically understand one or more programming languages, including languages such as Java, Python, and R that are commonly used for data engineering. Beyond the languages themselves, data engineers should be familiar with various libraries and frameworks for data engineering in the language of their choice. For example, the Python programming language has libraries such as pandas, scikit-learn, NumPy, PyTorch, and Matplotlib for data science and analysis. Data engineers may also be intimately familiar with both SQL and NoSQL databases and can use distributed systems like Hadoop.

All of this knowledge enables data engineers to work well with other data professionals in your organization, such as database administrators (DBAs)data architects, and data scientists. Data engineers often act as a “jack of all trades,” performing certain responsibilities that would ordinarily fall under these other roles as necessary. Above all, the job function of a data engineer is to build and maintain a robust and integrated data infrastructure for your organization. 

If this all sounds like a lot, you'd be right. But data engineers get compensated for their knowledge and hard work accordingly. According to Glassdoor, the average U.S. salary for data engineers is above $111,000, with top earners reaching as high as $163,000.

Related Reading: The 6 Soft Skills Data Engineers Need to Succeed

What is the Role of a Software Engineer?

Continuing with our “data engineer vs. software engineer” comparison, let's now look more closely at the role of a software engineer.

As the name suggests, software engineers are responsible for building, deploying, and maintaining software applications. These may range from traditional enterprise software applications to websites, mobile apps, and embedded systems. Software engineers apply their knowledge of computer science and programming languages, as well as standard engineering principles, in order to develop effective software in a productive and efficient manner.

Whereas all data engineers work with data, some software engineers may also specialize in data infrastructure and data pipelines. In this case, they are known as software data engineers, platform engineers, or infrastructure engineers.

Software engineers who work with data infrastructure also use similar programming languages and technologies as data engineers do: e.g. SQL, Amazon Web Services, Hadoop, and Spark. So, what separates software data engineers from data engineers? The primary difference is that software engineers take a more “macro” approach, while data engineers are more micro-focused.

More specifically, software engineers are responsible for building infrastructures such as schedulers, cluster managers, and distributed cluster systems. They also have to focus on implementing the code that makes these systems function more efficiently, which means that software engineers are usually stronger programmers than data engineers.

Software and data engineers typically work with similar programming languages, including Python and Java. However, other languages such as Scala and Golang may also be useful for software engineers, depending on the exact use case. Additionally, software engineers may need to work with DevOps tools such as Docker, Kubernetes, or a continuous integration/continuous delivery (CI/CD) tool such as Jenkins. These skills are critical to software engineers, who are continuously testing and deploying services in order to make business systems work faster and better.

Given the breadth of their work and knowledge, software engineers are also well-compensated, typically about the same as data engineers. According to PayScale, the average U.S. salary of a software engineer is over $87,000, with senior software engineers reaching an average of over $119,000. Software engineers’ salary depends on factors such as their level of experience, their industry, and their expertise.

3 Differences Between Data Engineers and Software Engineers

If your business is looking to build a robust data infrastructure, potentially incorporating data science and machine learning, you’ll likely need to employ both data engineers and software engineers, as well as a number of other roles. Although these jobs may seem similar on paper, they actually differ quite drastically in terms of their responsibilities.

Here's a quick rundown of what you need to know about the question of data engineers vs. software engineers.

1. They're complementary, but not interchangeable

Although the roles of software engineers and data engineers may share similar knowledge, it’s what they do with that knowledge that makes the difference—and that will greatly impact the efficiency of your business.

For instance, even if you assemble a great team of machine learning engineers or data scientists, the models they build need to run inside software applications, developed by software engineers who are capable of building the platforms they envision. 

By incorporating the right software engineers into your team from the start, you can facilitate communication between these various roles, so that everyone better understands the prerequisites necessary to support the models they are building.

2. Macro vs. micro

The question of “data engineer vs. software engineer” also comes down to the different approaches that the two roles take.

A software engineer primarily develops large-scale applications, platforms, and systems, especially those that are highly distributed and scalable. Because of their broader approach, software engineers are more common in smaller companies that don't have the capacity to hire for many roles. This makes software engineers a common pick for leaner teams.

However, software engineers aren't as strong as data engineers when it comes down to the nitty-gritty aspects of data engineering, data science, and analytics. For instance, data warehouses and querying data are two common weak points of software engineers, but it’s in areas like these where data engineers really shine.

3. Comparing the strong points

If you are searching for a person whose primary focus is on pulling data from an API or other data source, and then transforming it and moving it around, you're seeking a data engineer. Good data engineers have skills when it comes to querying and modeling data, as well as working in data warehouses and using visualization tools such as Looker and Tableau. However, if you want someone who is a strong coder and has experience wiring with DevOps tools, a software engineer would be the better choice.

Both options have strengths and weaknesses, pros and cons, and you can’t expect a single person to assume the responsibilities of both roles. Think about the tools your future team member will work with, and the tasks they’ll be performing, to help you make your decision.

Data Engineer vs. Software Engineer: Whom Should You Hire?

At the end of the day, it can be challenging for your organization to determine which title is best between a data engineer vs. a software engineer. It's not uncommon for even experienced hiring managers to post jobs looking for a data engineer when, in reality, the description is better suited to a software engineer or even a different role entirely.

It's important to understand the typical background and obligations of these roles so that you can pick the best person for the job. Of course, this isn't to say a data engineer isn't capable of working with Kubernetes or Docker; after all, engineers of all titles have found themselves in a time where they have to be proficient with a number of tools.

The most critical aspect you need to consider is the list of specific responsibilities you want your new team member to fill, which is the key deciding factor between a data engineer vs. software engineer. In many cases, teams would do best with both a data engineer and software engineer, along with a number of other roles.

Above all, remember that these roles are complementary, not interchangeable.

How Can Help with Data Engineering

Ultimately, your business is trying to build a more efficient data science department, and that means not only hiring the right people but supplying them with the right tools. Whether you choose a data engineer vs. software engineer (or both), make sure your technology stack allows them to make the most of their skills.

Businesses that work heavily with data need a tech stack that includes a powerful ETL (extract, transform, load) tool. ETL is the predominant form of data integration, helping users collect their data sources in a centralized location for easier queries, analytics, and reporting. So which is the best ETL tool to help your data engineers and software engineers make the greatest use of all your information, uncovering hidden trends and insights? is a powerful, feature-rich ETL and data integration tool that makes it easy for anyone to build automated pipelines between your data sources and your cloud data warehouse. With more than 100 pre-built connectors and integrations, and a user-friendly, drag-and-drop interface, helps businesses of all sizes and industries make smarter use of their enterprise data.

Want to learn more about how can become the most valuable part of your tech stack? Get in touch with our team of data experts today for a chat about your needs and objectives, or to start your 7-day demo of the platform.