Data engineer vs. software engineer: What’s the difference?
Data engineers vs. software engineers: They both write code. They both solve complex problems. But they focus on different layers of the tech stack.
Software engineers create the applications and features you see, touch, and interact with as an end user. Data engineers build the behind-the-scenes infrastructure that powers these apps, experiences, and even machine learning models.
Together, this dynamic duo writes code and builds systems that millions rely on daily. It’s a tight partnership. One builds the product. The other makes sure it runs on clean, reliable data.
If you’ve ever thought of becoming a data engineer or software engineer, this guide will help you break down what they do, where they overlap, and how they diverge. That way, you can decide on the path that fits your skills, interests, and goals.
Key points
- Data engineers build the pipelines and infrastructure that power data-driven products, while software engineers build applications and systems that people use every day.
- While there’s some overlap, tools and languages differ. Data engineers lean more on SQL and Spark, while software engineers often use Java, JavaScript, or C++.
- Collaboration looks different for each role. Data engineers typically work with data scientists, while software engineers tend to partner with designers and product teams.
- Career paths are flexible. With the right skills, software engineers can transition into data roles, and vice versa.
- Both roles offer strong job prospects and competitive salaries in fast-growing industries like tech, fintech, and health care.
What is a data engineer?
Data engineers design the systems that collect, organize, and move massive amounts of information. They build and maintain pipelines that transform raw data into clean, usable formats ready for analysis, reporting, or powering innovative technology like AI. Their work is the foundation for data scientists, machine learning engineers, and even app developers who rely on trusted data.
Key responsibilities include:
- Setting up database solutions
- Writing scripts to automate data movement
- Optimizing storage for speed and security
Without data engineers, companies wouldn’t have the reliable data they need to innovate and make informed decisions.
Learn more: Want to know what it takes to make it in data engineering? Check out Intuit’s guide on how to become a data engineer.
What is a software engineer
Software engineers build the applications and systems we use every day. They solve real-world problems by turning ideas into reliable, scalable technology. That might be a banking app, an online store, or a cloud platform.
The work software engineers do goes beyond writing code. They plan system architecture, troubleshoot bugs, and deploy updates to keep everything running smoothly. Some specialize in building the parts users see (frontend development), while others focus on servers, databases, and behind-the-scenes infrastructure (backend development).
Regardless of specialization, collaboration is key. Software engineers work closely with designers, product managers, and sometimes data engineers to bring complex ideas to life. Creativity, adaptability, problem-solving, and collaboration matter just as much as technical skill.
Learn more: Want to dig deeper into software engineering? Explore Intuit’s guide on how to become a software engineer.
Data engineering vs. software engineering: Key differences
While data and software engineers work with code and complex systems, their day-to-day focus looks different. Here’s a side-by-side look at how they compare:
| | Data engineers | Software engineers |
| Focus and responsibilities | Build and manage data pipelines, optimize databases, and ensure data is clean, accessible, and secure | Develop software applications and systems, design architecture, troubleshoot, and deploy updates |
| Tools and languages | SQL, Python, Spark, Hadoop, Apache Airflow, Amazon Web Service (AWS), Google BigQuery | Java, Python, JavaScript, C++, Kubernetes, AWS, React, Node.js |
| Skills | Data modeling, database management, ETL (extract, transform, load) processes, cloud computing, data warehousing | Software development, system design, debugging, version control (Git), DevOps practices |
| Collaboration across teams | Work closely with data scientists, machine learning engineers, and analytics teams | Collaborate with designers, product managers, QA (quality assurance) engineers, and sometimes data engineers |
| Experience needed | Strong foundation in data structures, databases, and distributed systems; bachelor’s degree in computer science or related field is common | Background in computer science, software development, and algorithms; degrees, bootcamps, and self-taught paths are all options |
| Salary and job outlook | Growing demand in industries like tech, finance, and health care; salaries competitive with software engineering, but can vary by company and location | High demand across tech sectors with strong salary potential; opportunities in startups, enterprises, and everything in between |
Salary and job outlook
Both roles offer strong salaries and job growth. According to Glassdoor, Software engineers in the US earn an average salary of nearly $112,000 per year, while data engineers average about $105,000, depending on experience and location. Demand for both is high, but data engineering is growing fast as companies scale their data operations.
Regardless of your path, both careers offer competitive pay, long-term stability, and opportunities across nearly every industry.
Tools and languages
Data engineering and software engineering‘s overall tools appear similar from afar, but their day-to-day stacks look different. Both might use Python, but software engineers rely more on Java, C++, JavaScript, and frameworks like React or Node.js.
Data engineers lean on SQL, Apache Spark, Hadoop, Airflow, and cloud platforms like AWS or Google BigQuery to build and manage pipelines.
Collaboration across teams
Both roles work across various teams, but who they work with differs. Software engineers typically collaborate with product managers, designers, and QA testers to ship customer-facing features. Data engineers align more closely with a data science role, partnering more often with data scientists, machine learning engineers, and analytics teams to ensure data is accessible and reliable.
Experience needed
Most software engineers and data engineers have a background in computer science or a related field, but it’s not one-size-fits-all. Many enter the field through college degrees, bootcamps, or even self-taught paths.
If you become a software engineer, it’s possible to make the switch to data engineering or add it to your toolbelt (or vice versa) down the road. All it takes is the right training and hands-on experience, and making the switch is possible.
Go deeper: Explore more types of engineering careers to decide which path is right for you.
Choosing the right tech career path for you
If you love building tools, solving problems, and shipping features people use every day, software engineering might be your lane. If you’re more interested in working with data, designing infrastructure, and supporting analytics at scale, data engineering might be more your forte.
Whichever path you choose, both careers offer strong salaries, growth potential, and the chance to make a real impact.
Explore data science and software engineering jobs at Intuit to see where your skills might shine.