Data engineers collect, organize, and prepare large amounts of structured and unstructured data for further analysis. They also design and build data pipelines and databases to manage the flow of volumes of raw information.

An essential part of the data industry, data engineers ensure that data scientists and analysts have what they need to do their jobs.

Some data engineers work on general, end-to-end data delivery tasks, while others focus on pipelines that connect data from distributed sources such as data lakes, warehouses, and databases. Some data engineers have a focus on database systems specifically.

Yes, the demand for data engineers and people with these skills is very high. The growth rate of data engineer jobs is projected at 21% between 2018 and 2028.

The rise of AI and machine learning solutions that help power the rapid management and analysis of data mean there’s a need for people who understand the evolving data landscape. Our courses and Data Engineer Certification are designed to build your skills and get you recruited.

It depends. If you enter the profession through the traditional pathway, it typically involves a Bachelor’s degree in computer science, perhaps followed by a Master’s. To study computer science, most degree programs require a basic understanding of calculus, algebra, statistics, and discrete mathematics.

You can also become a data engineer through a more modern pathway, such as online courses with providers like DataCamp, or by working in related data roles and building your knowledge of data engineering. In this case, math is certainly helpful, but it’s not a prerequisite.

Note that data engineers don’t use mathematics as much as data scientists or analysts. You don’t need to be a math whiz to design and create the systems that manage data, nor to collect, collate, and prepare it for others to analyze.

Yes, programming skills, especially in languages like Python and SQL, are essential for data engineering. These skills are used to manipulate data, automate processes, and build data pipelines.

Yes. Python, R, and SQL are the three most common programming languages data engineers use. Many are also skilled in other languages such as C++ and Java.

Even if you already know R and SQL, you stand a much better chance of landing a lucrative data engineering job if you know rudimentary Python - because it’s widely used, both in the data industry and in business.

No, DataCamp provides everything you need to learn data engineering on our dedicated platform. You just need a browser and a reliable internet connection.

After you sign up for one of our online courses, you’ll complete your exercises and projects on our browser-based platform.

Key skills for data engineers include proficiency in SQL, Python, data warehousing, ETL (extract, transform, load) processes, and cloud computing platforms like AWS, Azure, or Google Cloud.

DataCamp's courses help you learn data engineering by providing structured exercises, hands-on projects, and access to expert instructors. Our data engineering courses offer you the flexibility to build up your skills at your own pace.

We continuously update our courses so they reflect the latest technologies and best practices. We're also expanding our catalog of data engineering courses, projects and tutorials.

Think of a race car team. The data engineer builds the engine and ensures the fuel (data) flows smoothly. The data scientist drives the car and decides strategy based on the data. Engineers focus on architecture, scalability, and reliability, while scientists focus on analysis, algorithms, and predictions.

You will master the modern data stack. This includes SQL for querying, Python for scripting, Git for version control, and Shell for command-line tasks. You will also get hands-on experience with specialized tools like Apache Airflow (orchestration), Apache Spark (big data), dbt (transformation), and cloud services on AWS and Azure.

Yes. After completing the career track, you can take the Data Engineer Certification exams. This certification validates your ability to build pipelines, manage databases, and solve practical data problems, signaling to employers that you are job-ready.