What Does a Data Engineer Do?

As organizations generate increasingly large volumes of data, the need for professionals who can organize, manage, and prepare that data for analysis continues to grow. Individuals interested in pursuing a data‑focused career may be wondering, what does a data engineer do? And how does this role fit into the broader data science landscape? 

Data engineers build and maintain the systems that make data usable. These professionals solve technical problems and work closely with data scientists and analysts. For those wanting to enter or advance in this field, earning a master’s degree in data science can open the door to a wide range of opportunities. 

Data Engineer Role and Responsibilities

Data engineers build the systems — the pipelines, databases, and infrastructure — that allow organizations to collect, store, and move data. Their work ensures that data scientists and analysts have clean, reliable information, which is required for them to be able to uncover accurate insights and make good decisions. 

Data engineers are employed in many industries, including technology, finance, health care, entertainment, retail, manufacturing, and government. Any organization that relies on large‑scale data processing needs skilled data engineers to maintain and optimize their systems. Many data engineers work on cross‑functional teams alongside software developers, cloud architects, and data scientists.

To better understand what data engineers do, it helps to look at their core duties and responsibilities. They include: 

• Designing, building, and maintaining data pipelines that move information between systems
• Developing and managing databases, data warehouses, and data lakes
• Ensuring data quality through validation, cleaning, and transformation processes
• Collaborating with data scientists to understand their data needs and improve workflows
• Implementing security measures to protect sensitive information
• Managing cloud‑based data infrastructures using platforms such as AWS, Azure, or Google Cloud
• Monitoring system performance and troubleshooting issues
• Documenting data architectures and maintaining clear technical records
• Integrating new data sources into existing systems
• Supporting automation efforts to improve data processing efficiency

How to Become a Data Engineer

Becoming a data engineer typically involves gaining the right education, hands‑on experience, and specialized technical skills. The following step‑by‑step overview outlines a common pathway into the field.

1. Earn a Relevant Bachelor’s Degree

Most data engineers begin by earning a bachelor’s degree in a field such as computer science, information systems, data science, engineering, or mathematics. The programs for these degrees provide students with essential knowledge in programming, algorithms, and system design.

Students who want to pursue advanced roles often continue into a master’s program in data science or a related field, to deepen their technical expertise in areas such as data architecture, machine learning, and large-scale data processing. In competitive job markets, holding an advanced degree may help candidates stand out when applying for specialized or higher-level positions.

2. Build Strong Programming Skills

What data engineers do requires proficiency with programming languages such as Python, SQL, Java, and Scala. Being able to write efficient, scalable code is essential for these professionals as they build data pipelines and manage large datasets. Many individuals begin developing these skills through their college coursework, online tutorials, or personal projects. 

3. Gain Experience With Databases and Data Architecture

Understanding how data is stored, organized, and accessed is central to the role. Aspiring data engineers need to become familiar with relational databases (such as PostgreSQL or MySQL), NoSQL databases (such as MongoDB or Apache Cassandra), and data warehousing concepts. Hands‑on experience with these tools helps professionals build their technical fluency. 

4. Learn Cloud Platforms and Big Data Tools

Modern data engineering relies heavily on cloud technologies. Platforms such as AWS, Azure, and Google Cloud provide tools for data storage, processing, and automation. Big data frameworks such as Hadoop, Spark, and Kafka are also widely used in large‑scale data operations. Experience with these tools can strengthen a candidate’s qualifications in the job market. 

5. Pursue Internships or Entry‑Level Roles

Internships, apprenticeships, and junior-level technical roles provide professionals with valuable real‑world experience. Many data engineers begin their careers as data analysts, software developers, or database administrators before transitioning into engineering roles. These positions help them build practical skills and expose them to the workflows used in professional data environments. 

6. Consider Earning a Professional Certification

Holding a certification can demonstrate a professional’s expertise in working with a specific tool or platform. Popular options include the AWS certified data engineer, Google professional data engineer, and Microsoft Azure data engineer associate credentials. While not always required, a certification can indicate a professional’s readiness for more advanced responsibilities. 

7. Continue Developing Skills Through Projects and Practice

Data engineering is a rapidly evolving field. Building personal projects, contributing to open‑source tools, or experimenting with new technologies can help professionals stay current. Ongoing skill development is important for data engineers’ long‑term success. 

Data Engineer Salary and Job Outlook

The data engineering field offers a strong earning potential and a promising job outlook. According to Payscale’s 2025 data, the median salary for data engineers in the United States is approximately $100,000 per year. Depending on their industry, location, and technical specialization, professionals in the top 10% of earners make upwards of $142,000 per year, according to Payscale.

Job outlook data points to continued demand for data engineers. StartUs Insights reports that the global data engineering market is expanding rapidly as organizations invest in cloud computing, automation, and advanced analytics. Its 2025 data engineering industry report notes a year-over-year increase of more than 20,000 jobs. 

Prepare for a Data Engineering Career Through Advanced Education

You now know what a data engineer does. The next step in your journey is getting qualified for the role.

If you’re exploring how to build the skills and competencies needed for this field, the online Master of Science in Data Science (MDS) program from Tseng College at California State University, Northridge (CSUN), offers a strong foundation. The program’s curriculum emphasizes practical, industry‑aligned training in programming, data management, analytics, and cloud technologies, all of which are directly related to data engineering roles.

With guidance from experienced faculty and opportunities to work on real‑world projects, you can develop the technical and analytical abilities needed to pursue a career in this growing field.

Learn more about CSUN’s program and students’ learning outcomes to see how it can help you reach your professional goals.