What is a data engineer, and what do they do?
Your online orders. Your favorite playlists. Even your personalized news feed. It all runs on data. But raw data on its own doesn’t deliver those experiences. It has to be collected, cleaned, and moved into place. That’s where data engineers come in.
But what is a data engineer, exactly? Simply put, they’re the people who make sure data gets where it needs to go in a clean, reliable format that’s ready for action. They analyze and manipulate this data to build the systems that power everything from business insights to machine learning models. Without data engineers, data scientists, analysts, and developers wouldn’t have the tools they need to do their jobs.
Think of data engineers as builders who turn scattered information into something smart, useful—something you can act on. In this guide, we’ll dive into exactly how they do that, how they stand out from similar roles on development teams, and what a role in data engineering might look like for you.
Key points
- Data engineers build the systems that collect, clean, and move data, so teams like data scientists and developers can turn it into insight.
- Whether it’s for powering machine learning or feeding real-time dashboards, data engineers make sure information is reliable and ready to go.
- Data engineering isn’t the same as data science. Scientists analyze. Engineers build the infrastructure that makes analysis possible. Both are essential, but the jobs are different.
- You don’t need a degree to get started. With online courses, bootcamps, and hands-on projects, anyone with the right mindset can begin learning data engineering.
- The data engineering job market is hot. Any industry that relies on digital tools needs data engineers to keep their systems running strong. That includes fintech, health care, logistics, e-commerce, and more.
What does a data engineer do?
Data engineers work behind the scenes to turn raw data into the applications we use every day. On a typical day, a data engineer might:
- Build data pipelines that move data from one system to another, such as from a mobile app to a data warehouse.
- Design and maintain databases that can store billions of rows of information.
- Make sure data is reliable and accurate, so when teams analyze it, they’re working with the real picture and not broken numbers.
They work with tools like SQL, Python, Apache Airflow, Spark, and cloud platforms like Amazon Web Services (AWS), or Google Cloud. It’s a unique mix of technical and creative work with code to solve the world’s problems.
A common myth is that data engineers just “clean up” data. The truth is their job is far more strategic. Cleaning raw data may be part of their job, but their role is ultimately to make data effective.
What is data engineering?
Data engineering is the practice of designing systems that gather, store, and transform data into formats that are easy to analyze and use. Think of it like prepping ingredients in a professional kitchen. Before a chef can cook, someone has to wash, chop, and portion every tomato, onion, and pepper. In the same way, data engineers organize and optimize data so other teams can get to work.
That prep work powers nearly everything. Dashboards that track performance. AI models that deliver personalized recommendations. Real-time analytics that help businesses move fast and make smart decisions.
And it’s not just about moving data around. Great data engineering makes machine learning, business intelligence, and modern software possible. It gives teams the clean, trusted data they need to build, test, and create.
How does data engineering differ from data science or software engineering?
Data engineers, data scientists, and software engineers all work with data, but in different ways. Data engineers build the systems that gather and prep data. Data scientists use that data to find patterns, make predictions, and run experiments. You could say data engineers build the highway, and data scientists drive on it.
Then there’s software engineering. Like data engineers, software engineers write code, but their focus is on building apps and systems people interact with directly. Data engineers drive what’s happening behind the scenes—databases, pipelines, storage, and infrastructure.
There’s overlap, sure. All three roles involve coding, logic, and architecture. But their goals are different:
- Data engineers care about speed, scale, and reliability.
- Data scientists care about insight and accuracy.
- Software engineers care about usability and experience.
Want to dig deeper? Check out our full breakdown of the difference between data and software engineers.
Skills and tools needed for data engineering
To succeed in data engineering, you don’t need to know everything, but there are a few key skills that show up in most job descriptions:
- Programming languages like Python, SQL, and sometimes Java or Scala
- Data pipeline tools such as Apache Airflow or Kafka
- Databases and warehouses, including PostgreSQL, Snowflake, BigQuery, or Redshift
- Cloud platforms like AWS, Google Cloud, or Azure
- Foundational knowledge of data modeling, extract, transform, load (ETL) processes, and data governance
Soft skills matter, too. Clear communication, problem-solving, and attention to detail go a long way—especially when your work is powering critical business decisions.
Want to start building these skills? Check out our guide on how to become a data engineer.
Where data engineers work: Industries and demand
Data is the currency of the information economy. As such, data engineers are in demand virtually everywhere, but industries like fintech, e-commerce, health care, logistics, and AI-driven startups appear to be leading the charge. These companies rely on accurate, fast-moving data to drive decisions, personalize customer experiences, and stay ahead of the competition.
If a company is growing its digital presence, it’ll need a strong data infrastructure and someone to build it. The result is a booming job market.
The US Bureau of Labor Statistics projects 9% growth for data engineering roles through 2033. That’s much faster than the average for all occupations. As far as salary, data engineers average about $105,000 annually, depending on experience and location.
Businesses need data engineers to design systems that keep up with real-time data, massive scale, and complex data privacy needs.
How to learn data engineering
You don’t necessarily need a computer science degree to start working in data engineering. But you need to be curious and consistent with your learning.
How you break into the industry will depend on your individual circumstances, but some of the fastest ways to get started include:
- Online bootcamps and certification programs focused on cloud data engineering or big data tools
- Hands-on projects using open datasets to build pipelines, transform data, and publish results
- Massive open online courses (MOOCs) and YouTube channels that teach topics like SQL, data modeling, and Python scripting
- GitHub repositories where you can explore real-world code and collaborate with others
Fortunately, you can learn a lot for free. The internet’s packed with resources, communities, and tutorials designed for beginners and career-switchers alike. Start small, stay consistent, build real projects, and over time, you’ll begin to see tangible progress in your skills and your career.
Is data engineering the right career for you?
If you love solving problems, spotting patterns, and building the systems that make everything else run, data engineering might be for you.
Pursuing a data engineering role is a great fit for people who:
- Enjoy working with code and data
- Like organizing complex systems and finding efficient solutions
- Want to build something that powers real-world decisions
Data engineering isn’t always flashy, but its impact is everywhere. From personalized recommendations to real-time fraud detection, your work becomes the foundation other teams rely on.
Explore data science jobs at Intuit to see how your skills could help shape the future of data-driven innovation.