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So you're looking for a reliable, in-demand, and well-paid career. Data engineering deserves a close look. Data is already the backbone of numerous industries, and as companies adapt to a digital age that's increasingly shaped by AI, data is going to be gold. Data engineers play a key role in turning that gold into value for companies. The stats back up just how vital these professionals are:
- The US Bureau of Labor Statistics doesn't track data engineers as a separate occupation, but database architects, the closest match, are projected to grow 9% from 2025 to 2035, three times the 3% average for all occupations.
- 22.8% of the Indeed data engineer postings that 365 Data Science analyzed don't specify a degree level, so non-traditional applicants can compete.
- $135,000 is the median total annual pay for data engineers, according to Glassdoor.
Data engineering is a prime field. It's growing, it's open, and it's well-paid. So now, you're probably already planning your first steps. We can help. Read on for everything you need to know about how to become a data engineer.
What is data engineering?
Data engineering is the field of tech work that handles data infrastructure. It's shaped by the tools and systems that help organizations gather, store, process, analyze, and manipulate data. The goal is to make sure a company doesn't just have data but can actually take advantage of it.
What does a data engineer do?
A data engineer plans, builds, and maintains systems that bring in, store, and process data. Think of them as the plumbers of the data world. Their job is to pipe data from one place to another and make sure it's clean, so the company can rely on it to reach its business goals.
In short, they build the infrastructure that lets data professionals downstream do their jobs. But let's get more detailed. Here are some common data engineer job responsibilities:
Data engineer responsibilities
Before we get into data engineer roles and responsibilities, one piece of advice: read job descriptions. Seriously. Companies differ on what fits under "data engineering," so the role varies widely. With that said, some things hold true across most data engineering roles:
- Crafting and maintaining data pipelines
Data engineers spend a good deal of their time on extract, transform, load or extract, load, transform processes (you'll see them listed as ETL/ELT). Basically, they take raw data and make it usable for their organizations.
- Designing data models and database architecture
This is the corollary to the previous entry. As part of refining data, data engineers structure it with data models to make it consistent and clear. They also choose storage (such as PostgreSQL, Snowflake, MongoDB, or Delta Lake, depending on the use case) and define patterns so the data stays accessible.
- Collaborating with data scientists/analysts as well as other stakeholders
To build models, data scientists need clean training data. To find good insights, data analysts need well-built data warehouses. And at a larger scale, every data role works toward business goals alongside leaders and other stakeholders. All of that relies on good data, which is the core job of the data engineer. That means data engineers need to collaborate well with both the data team and other experts across the company.
- Seeing to data security and governance
This is a major responsibility. Data engineers often handle private data that shouldn't leak, so they keep it safe with access control, encryption, masking, and anonymization. They also maintain metadata and document data lineage, often with in-house governance teams. All of this keeps the company compliant with frameworks such as GDPR, HIPAA, and SOC 2.
Data engineer vs data scientist
Data engineers differ from data scientists in what they do with data. Data engineers build the digital pipes and storage tanks that get data to data scientists. Data scientists then use that data to build models that generate insights and help the business reach its goals.
Data engineers do what their name suggests: they're more on the engineering side of things. The role emphasizes software development, database management, and system architecture. Data scientists, by contrast, apply statistics and machine learning to the data engineers make usable, then build models that guide decision-making.
Data engineer or data scientist? Find the path for you with our free career quiz.
Why become a data engineer: Career outlook and salary
Job market demand in the US
To say it bluntly: data engineers are in high demand. BLS projects about 7,300 openings a year for database administrators and architects through 2035, and that's only the closest official category. Demand comes from industries as diverse as tech, healthcare, finance, and e-commerce, all of which need people who can keep their data flowing.
You'll find data engineers at the tech heavyweights you'd expect, such as Adobe, Netflix, and Google, but also across the business spectrum, from tiny startups to growing mid-sized companies. It's a specialization that can take you where you want to go, and in-demand expertise gives you more say in where you end up (maybe even remote).
Salary expectations
All of that leads to good salaries in the US. As we mentioned in the intro, the overall median total pay is $135,000, according to Glassdoor. Compare that with the Bureau of Labor Statistics median for computer and information technology occupations in general, $109,470, and the median for all occupations, $50,980 (both May 2025). That's a nice bump from both.
But that's just the median. Here's how data engineer pay changes with experience, according to Glassdoor:
Note: Because of how Glassdoor organizes its data, the table above covers only roles with the exact title "data engineer." Look under "senior data engineer" and the median total pay jumps to $171,000. One step above that, "principal data engineer" has a median total pay of $208,000, so there's plenty of room for income growth.
Geographic variations
Experience isn't the only thing that affects pay. Location does, too. Here's what you can expect to make as a data engineer in different locations:
Data engineer career paths
So far, we've focused on data engineering itself, but as you move through this career, you'll have the chance to specialize or follow a leadership track. Here's what the salaries for these roles look like:
Essential data engineering skills
If you're still wondering what to study to become a data engineer, we've boiled down the key skills you'll need to land that first data engineering job. Here are the skills needed for a data engineer position:
Programming languages
- Python: This is a cornerstone skill for data engineering. Python is a versatile language that handles everything from scripting to automation to data processing.
- SQL: This language lets you query and manage databases, and since databases are a big part of a data engineer's work, it's crucial.
- Java/Scala: Knowing one of these helps you work with big data and distributed data frameworks, so skills here will help you keep growing.
Already writing some Python? See where you stand with our Python quiz.
Database management
- Relational databases: This includes tech such as PostgreSQL and MySQL. You should be fluent in these tools for schema design, normalization, and indexing.
- NoSQL databases: MongoDB and Cassandra, for example, are useful when you need to handle unstructured data.
- Data warehousing: Mastering Snowflake, Amazon Redshift, or Google BigQuery will give you the skills you need here.
ETL processes and data pipeline tools
- ETL/ELT processes: This is the backbone competency you'll apply almost daily, so skills here are crucial.
- Data pipeline tools: Apache Airflow, Apache Kafka, and Apache Spark might all become vital tools in your day-to-day work, so learning how to use them will serve you well.
Cloud platforms
- AWS: As the October 2025 outage showed, much of the internet runs on this platform, so skills in S3, Glue, Redshift, EMR, or Lambda will be helpful.
- Google Cloud Platform (GCP): Another major cloud platform. Knowing BigQuery, Dataflow, and Pub/Sub will let you make full use of it.
- Microsoft Azure: This is common in enterprise settings. Knowing Data Factory, Azure Databricks, or Microsoft Fabric (which Microsoft now positions as the next step for Synapse Analytics users) will give you the skills to work with it.
Additional tech skills
- Version control: Using Git/GitHub to track and manage changes to code.
- Linux/Unix command line basics: A working familiarity here gives you flexibility across your tech stack.
- Basic understanding of distributed systems: You'll be pulling and sending data all over the place. Knowing the technical fundamentals will help you do that efficiently.
Soft skills
- Problem-solving: You'll face unexpected and perplexing problems, so the ability to come up with creative solutions will take you far.
- Communication: Collaboration is a cornerstone of this job, so getting your ideas across and hearing what others have to say are both vital.
Note: This is a general overview. Depending on where you end up, the company may use a different tech stack. If you're drawn to one specific cloud platform, for example, you can match your skills to a job's requirements. Once again, we encourage you to look at vacancies on LinkedIn, find the work you'd like to take on, and see what skills those descriptions ask for.
How to become a data engineer in 2026
Build your data foundation faster with a structured program
If you'd rather have structure and career support, a program that covers the foundations can speed up the first stretch. TripleTen's Data Analytics program covers SQL, Python, and data modeling in 4 months. A data analyst role can also be a stepping stone into data engineering, since both run on SQL and Python. Here's how the two routes compare:
Already have 2+ years of production experience as a software engineer or data professional? TripleTen's AI & Machine Learning program is a 20-week accelerator for moving into ML engineering. Start with the skills audit to see if you qualify.
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