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How to become a data engineer

Last updated: 2 September 2025

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Quick summary of this article

Are you interested in a career as a data engineer? Here are the key points you need to know:

  • Data engineering involves computer science and software engineering for big data.

  • Data engineers build pipelines, integrate sources, clean data, optimise data warehouses, and troubleshoot.

  • Steps to becoming a data engineer: degree education, developing skills, experience, continuous learning, and networking.

  • Useful degree areas for a career in data engineering include computer science, information systems, data science, and data management.

  • Data engineers are in high demand with salaries in the UK ranging from £45k to £115k.

Looking to advance your career in data? How about becoming a data engineer?

With the exponential growth of data in today's digital world, the demand for skilled data engineers is on the rise. Organisations across industries, from finance to healthcare to marketing, rely on data engineers to make sense of the vast amounts of information they generate and collect. Not surprisingly, LinkedIn placed data engineer 13th on its list of Jobs on the Rise 2023.

If you have a passion for problem-solving, a curiosity for data, and a knack for technology, becoming a data engineer might be the perfect career path for you. Explore the field of data engineering, what it actually means, and the steps you can take to become a data engineer. Whether you're a full-time working professional considering a career change or an undergraduate student planning your future, we've got you covered.

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What is data engineering?

Data engineering is a crucial field that involves data analysis, computer science, and software engineering. Data engineers create, implement, and maintain the infrastructure, systems, and pipelines necessary to process and transform large sets of data. Think of a data engineer as the architect and builder behind the scenes, ensuring that data is captured, stored, and accessible for analysis and insights.

What does a data engineer do?

Data engineers work closely with data scientists and data analysts to understand their requirements and translate them into scalable data solutions. They are responsible for building data pipelines, integrating various data sources, cleaning and transforming raw data, and optimising the data warehousing and retrieval processes. In essence, they create the framework that enables organisations to derive valuable insights from their data.

The role of a data engineer can vary depending on the organisation and industry they work in. However, some common responsibilities include:

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    Building and maintaining data pipelines

    Data engineers are experts at designing and implementing data pipelines that efficiently process and transform data, using tools and technologies such as Apache Spark and SQL to ensure smooth data flow.
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    Integrating data sources

    A skilled data engineer is an expert at integrating different data sources, including data warehouses, APIs, and data streaming systems to ensure the integrity and reliability of the data being collected.
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    Cleaning and transforming data

    Data engineers are responsible for cleaning and transforming raw data into a usable format. They apply techniques like data normalisation, aggregation, and quality control to ensure data accuracy.
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    Data storage and retrieval

    Data engineers optimise data storage and retrieval processes so that analysts and data scientists can efficiently access and retrieve the required information. They often implement database management through data warehouses and cloud storage solutions.
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    Troubleshooting and performance tuning

    These are key responsibilities of a data engineer as it ensures smooth data operations, empowers timely and reliable data analysis, and supports organisations in making informed decisions based on high-quality data.
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    Collaboration with data teams

    Data engineers collaborate closely with data scientists, data analysts, and other specialists to provide them with the infrastructure and tools they need to analyse data. This collaboration drives data-informed decision-making in organisations.

How to become a data engineer

If you're interested in a career in data engineering, here are the steps you can take to get started:

1. Education and qualifications

A strong foundation in computer science, information systems, or a related field is beneficial if you're looking to pursue a future career as a data engineer.

A postgraduate degree can help you build advanced programming and data skills from scratch. At Walbrook, our online MSc programmes are designed to help career changers or upskillers build core technical confidence, with flexibility to study around your schedule.

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2. Gain technical skills

Data engineering requires a strong knowledge of programming languages such as Python, Java, or Scala. You'll also want to familiarise yourself with database systems, SQL, big data processing frameworks, and cloud computing platforms like AWS or Azure.

If you're looking for a degree to develop your data engineer skills, explore the modules on offer to make sure you can develop proficiency in these systems and platforms.

3. Hands-on experience

Build your portfolio by working on practical projects that involve data management, integration, and analysis. Open-source projects are often available online and are an invaluable way to get practical experience while also giving you the opportunity to network with other data engineers.

At Walbrook, you'll apply your skills through hands-on coursework and simulations built into the programme. You'll also get feedback from academic staff and peers to help you grow your confidence in real-world problem-solving.

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4. Continuous learning

In a rapidly evolving field, it's important stay up-to-date with the latest technologies, tools, and data trends. Many data engineers follow industry publications, blogs, and websites to stay informed about the latest advancements and best practices in the field.

By embracing continuous learning, aspiring data engineers can expand their expertise, enhance their problem-solving abilities, and remain competitive in the job market.

5. Professional networking

Connect with professionals in the data engineering field through networking events, industry conferences, or online communities. Joining professional associations or LinkedIn groups can also provide opportunities to connect with experts and senior data engineers to learn from their experiences.

Networking can open doors to mentorships, job opportunities, and valuable insights about the industry. At Walbrook, you'll also benefit from online employer events, career workshops, and access to a dedicated jobs board to help you connect with hiring organisations while you study.

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What qualifications do I need to be a data engineer?

Having a strong educational background in fields like computer science or data science can prepare you for a career in data engineering. Below are a few degree subjects that can get you started on your career journey:


  • Computer science

    Computer science degrees give you essential programming skills and a deep understanding of algorithms and data structures, which are crucial for data engineering. It’s one of the most versatile routes into the profession, especially if you're looking to combine theory with applied skills. At Walbrook, our MSc Computer Science degrees are designed for non-computing graduates and provide a direct pathway into data engineering and other technical roles.


  • Data science

    A degree in data science provides a comprehensive understanding of statistical data analytics, machine learning, and data visualisation, which complement the technical aspects of data engineering. Our MSc Computer Science with Data Science helps you combine data principles with specialist analytical and decision-making techniques.


  • Information systems

    A business information systems degree combines business knowledge with technical expertise, giving you insights into how data is managed and used by data engineers in organisations.


  • Artificial intelligence

    Although AI may seem a step removed from data engineering, it relies heavily on structured, accessible and high-quality data. Studying AI can give you a better understanding of the systems and models your infrastructure needs to support. Walbrook’s MSc Computer Science with AI covers both foundational computing and advanced AI topics, making it ideal if you're interested in smart systems and scalable data design.

Data engineers, data scientists and data analysts: key differences

Understanding the distinctions between data engineers, data scientists, and data analysts is crucial for anyone looking to enter the field of data engineering or any related discipline.

Data engineers


  • Focus areas

    Data engineers specialise in designing and building systems to manage and process large sets of data. Their primary responsibilities include creating data pipelines, ensuring seamless data integration, and maintaining data architectures. They focus on data quality, data governance, and making sure the data infrastructure is reliable and efficient.


  • Key skills

    To become a data engineer, it helps to know programming languages like Python, Java, or Scala. Mastery of big data tools like Apache Spark and Hadoop is also useful. Data engineers also need to understand concepts related to data warehousing, database management, cloud computing, and big data processing frameworks.


  • Tools

    Typical tools and technologies used in data engineering include SQL, data integration tools, workflow scheduling tools, and data streaming tools. Ensuring the optimisation and performance tuning of data pipelines are key activities for data engineers.

Data scientists


  • Focus areas

    Data scientists primarily engage in data analysis and apply machine learning algorithms to extract insights from data. They focus on building models that predict future trends, identify patterns, and solve complex business problems through data analytics. Unlike data engineers, they delve deeper into the creation of statistical models and the interpretation of data.


  • Key skills

    Proficiency in machine learning, data visualisation, statistics, and programming languages such as Python or R is essential. Data scientists need to be adept at both exploratory data analysis and the deployment of machine learning models into production environments.


  • Tools

    Data scientists often use big data tools, machine learning frameworks such as TensorFlow or PyTorch, and data visualisation tools like Tableau or Power BI. They also work closely with cloud computing platforms for scalable machine learning solutions.

Data analysts


  • Focus areas

    Data analysts are focused on interpreting and presenting data to help inform business decisions. Their role is less technical compared to data engineers and data scientists but is equally important for data-driven decision-making processes.


  • Key skills

    Strong analytical and problem-solving skills are essential. Data analysts need to be proficient in SQL, data visualisation, and sometimes basic programming or scripting languages. While they don't typically engage in maintaining data architectures, they are crucial in translating data into actionable insights.


  • Tools

    Common tools used by data analysts include SQL databases, Excel, and data visualisation tools like Tableau or Power BI. They are often responsible for regular reporting and dashboard creation to visualise key metrics.

Each of these roles – data engineers, data scientists, and data analysts – plays a unique and integral part in the data ecosystem.

If you aspire to become a data engineer, focus on mastering data engineering skills, big data tools, and understanding data architecture. Data engineers ensure the foundation of data is solid, whereas data scientists and data analysts build on this foundation to derive insights and inform decisions using data analytics and machine learning.

Take the next step towards your career in data engineering

Study 100% online at Walbrook

With the right mix of technical knowledge, hands-on experience, and industry insight, you can carve out a rewarding future in data engineering. If you’re ready to build your expertise and step into the world of data pipelines, cloud platforms, and large-scale systems, our MSc Computer Science with Data Science could be the perfect next step.

Develop core computer science skills while specialising in big data processing, machine learning, and real-world data applications. No computing background required.

Explore MSc Computer Science with Data Science
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