What does a Data Engineer do?

A Data Engineer builds data pipelines to collect and process information from multiple sources. These data streaming systems allow businesses to perform in-depth analyses and answer complex questions.

As more businesses embrace data-driven decision-making, the demand for Data Engineers will continue to grow. That means there are plenty of promising opportunities for people looking to upskill core data-based capabilities. In this guide, we’ll explore the role of Data Engineers, their key responsibilities, salaries, and more.

A Data Engineer is responsible for building, managing, and fine-tuning an organisation’s data infrastructure. They create data pipelines that gather raw information and transform it into a usable format for data analysis.

As the amount of data grows exponentially, businesses rely on Data Engineers to sift through the noise and collect the most valuable information. Without these professionals, organisations would struggle to make sense of a vast sea of raw data in different formats.

For example, a retailer might want to understand why clients prefer certain products. The business could analyse many types of data, from customer reviews to purchase history and returns. A Data Engineer speeds up this process by collecting the relevant data points, organising them, and delivering them to Data Analysts for further evaluation.

Data engineering is the foundation of data science, which includes several similar but distinct career paths. Data Engineers focus on developing data pipelines to extract and organise information. This processed data is then analysed by Data Analysts to answer questions and detect trends. Some businesses also employ Data Scientists, who perform more complex statistical analyses and develop predictive models. In the above scenario, a Data Scientist could use historical customer data to predict future buying behavior.

A Data Engineer’s exact responsibilities can vary by role, but here are a few common tasks:

Data Engineers use various tools and techniques to accomplish these fundamental tasks. For example, extract, transform, and load (ETL) processes allow them to combine both structured and unstructured data into a centralised location. Many professionals also rely on cloud infrastructure platforms — such as Amazon S3 and Google Cloud Storage — to store vast datasets remotely.

If you’re interested in a career in data engineering, you’ll need these essential technical skills:

Of course, hard skills aren’t enough to excel in this field; strong soft skills are just as critical. Many data engineering projects are highly collaborative, requiring close collaboration with people from different departments. For example, a Data Analyst might ask you to aggregate information for business intelligence reports. Effective communication helps you simplify complicated concepts and build positive relationships with all team members.

You’ll inevitably encounter challenges, especially when dealing with complex or messy data, so excellent problem-solving skills are a must. Being adaptable will also enable you to learn new techniques and master the latest technologies.

Certifications can help you gain new skills and demonstrate your expertise to potential employers. Consider pursuing industry-recognised credentials, such as AWS Certified Data Analytics(opens new window) and Google Professional Data Engineer(opens new window).

In 2025, it’s predicted that users will generate and consume a staggering 182 zettabytes of data(opens new window). Traditional data processing and analytics methods simply can’t keep up with this exponentially growing flood of information.

That’s where Data Engineers come in. They develop infrastructure to collect the right information and make the data usable for analysis. This process allows leaders to make strategic business decisions based on accurate and relevant data, rather than a random jumble of information.

Many industries rely heavily on data engineering for high-stakes decision-making. For example, finance institutions(opens new window) use fraud detection systems to automatically identify and block suspicious transactions. Similarly, data engineering empowers healthcare organisations(opens new window) to analyse patient data and improve the quality of care. Without this field, businesses would struggle to analyse information and address emerging issues effectively.

Even the most experienced Data Engineers can’t manually process millions of data points. Instead, they rely on sophisticated tools to automate and streamline data workflows.

Apache Hadoop is one of the most critical technologies, using parallel processing to handle big data efficiently. Many professionals also turn to Snowflake for building batch data pipelines quickly, while Apache Airflow allows users to design and automate workflows. These technologies can save significant time and reduce the risk of errors.

There’s no one-size-fits-all approach to becoming a Data Engineer. You can use many strategies to gain the necessary knowledge and skills, giving you the freedom to shape your professional journey.

It all starts with developing foundational programming and database skills. Most Data Engineers rely on SQL to manage databases, while Python is commonly used for routine tasks like data storage and processing. Online courses or coding bootcamps can help you learn these languages quickly.

Beyond these basic skills, a Multiverse apprenticeship is one of the best ways to expand your data engineering knowledge. Our three-year Advanced Data Fellowship teaches you how to analyse information and design innovative data solutions. It covers advanced topics like data governance and predictive analytics. You’ll also gain hands-on experience by applying your newfound skills in your current role.

Once you’ve gained the necessary skills, start looking for entry-level roles like Business Data Analyst or Junior Data Engineer. These positions will allow you to refine your skills further and may lead to more advanced positions as you gain experience.

For example, Daniel Beach(opens new window) began his career in a completely unrelated field — agriculture. In 2013, he decided to pivot to data engineering and taught himself SQL. With this self-taught knowledge, he landed a Data Analyst position at a bank. From there, he quickly climbed the career ladder, moving from Senior Data Analyst to Business Intelligence Engineer, then to Data Engineer, and finally to his current role as Senior Data Engineer.

Data engineering is a vast field with ample opportunities for advancement and specialisation. Here’s one possible career trajectory, with salary data from Glassdoor.

Average UK salary: £41K

A Data Analyst uses statistical methods and software to gather and study data. They search for meaningful trends in datasets and communicate their insights to stakeholders. For instance, they might create data visualizations or dashboards to present their findings in accessible formats.

Average UK salary: £31K

A Junior Data Engineer typically works under the guidance of more experienced data professionals. Their responsibilities include fundamental tasks like collecting, cleaning, and storing data. They may also assist with building and optimizing data pipelines and troubleshooting issues.

Average UK salary: £48k

A Data Engineer handles more advanced tasks, such as designing sophisticated data pipelines and solving business problems autonomously. This mid-level role typically requires two to five years of experience in data engineering.

Average UK salary: £46k

A Big Data Engineer specialises in developing and managing pipelines for big data. These systems must be scalable and capable of handling vast, ever-increasing amounts of information without experiencing performance issues. Big Data Engineers often collaborate with Machine Learning Engineers and other professionals who rely on big data for analysis and decision-making.

Average UK salary: £72K

As the name suggests, a Lead Data Engineer oversees a team of engineers. They plan complex data architecture, delegate tasks, and manage large-scale projects. They’re also responsible for mentoring junior employees.

Once a niche field, data engineering has become an integral part of modern business operations. It’s the foundation of advanced analytics and data management, helping businesses get the most out of their data.

Take the next step on your upskilling journey with a Multiverse apprenticeship. Our Data Fellowship programmes teach fundamental technical skills, such as data visualization and machine learning. You’ll strengthen your data engineering knowledge as you complete our online curriculum and collaborate with peers. Plus, you’ll receive personalised career coaching from industry experts. All without having to put your career on hold.

Ready to take the leap? Get started today by completing our quick application(opens new window).