What Is a Data Engineer?: A Guide to This In-Demand Career

Written by Coursera Staff • Updated on

Big data is changing how we do business and creating a need for data engineers who can collect and manage large quantities of data.

[Featured Image] A data engineer works at a desktop computer in an office.

Data engineering is one of the fastest-growing job titles in the UK over the past 5 years, coming in at 13 on LinkedIn’s list of most in-demand jobs in 2023 [1]. With the digitalisation of our society rapidly increasing, it’s no surprise this data-oriented profession is rising in popularity. 

Data engineering is the designing and building systems for collecting, storing, and analysing data at scale. It is a broad field with applications in just about every industry. Organisations can collect massive amounts of data, and they need the right people and technology to ensure it is in a highly usable state by the time it reaches data scientists and analysts.

In addition to making the lives of data scientists easier, working as a data engineer can allow you to make a tangible difference in a world where we’ll be producing 463 exabytes per day by 2025 [2]. An exabyte is a one and 18 zeros of bytes worth of data. Fields like machine learning and deep learning can’t succeed without data engineers to process and channel that data.

What does a data engineer do?

Data engineers work in various settings to build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. Their ultimate goal is to make data accessible so that organisations can use it to evaluate and optimise their performance.

These are some common tasks you might perform when working with data:

  • Acquire data sets that align with business needs

  • Support the development of data streaming systems

  • Implement new systems for data analytics and business intelligence operations

  • Develop business intelligence reports for company advisors

  • Develop algorithms to transform data into useful, actionable information

  • Build, test, and maintain database pipeline architectures

  • Collaborate with management to understand company objectives

  • Create new data validation methods and data analysis tools

  • Ensure compliance with data governance and security policies

Working at smaller companies often means taking on a greater variety of data-related tasks in a generalist role. Some bigger companies have data engineers dedicated to building data pipelines, while others focus on managing data warehouses—populating warehouses with data and creating table schemas to keep track of where data is stored.

What’s the difference between a data analyst and a data engineer?

Data scientists and data analysts analyse data sets to glean knowledge and insights. Data engineers build systems for collecting, validating, and preparing high-quality data. Data engineers gather and prepare the data, and data scientists use the data to promote better business decisions.


Why pursue a career in data engineering?

A career in this field can be both rewarding and challenging. You’ll play an important role in an organisation’s success, providing easier access to data that data scientists, analysts, and decision-makers need to do their jobs. You’ll rely on your programming and problem-solving skills to create scalable solutions. Data engineers take on many responsibilities, applying diverse technological and human skills to work with diverse teams and develop innovative solutions.

Read more: Data Analyst vs. Data Scientist: What’s the Difference?

In addition to diverse job responsibilities and a fast-paced environment, data engineers have many opportunities to grow. Many qualified candidates begin in junior data engineering positions before moving to more advanced roles such as senior data engineer, lead data engineer, and head of data engineering. This allows data engineers to continually develop and refine their skills, providing opportunities within organisations to expand their responsibilities, opportunities, and annual earnings. 

Data engineer salary

Data engineering is a well-paying career because of the high level of technological skill and need for advanced training. According to Glassdoor, the average salary in London, UK, is £58,058 as of August 2023 [3]. Individual salaries vary based on location, experience, company size, and job responsibilities. 

How to become a data engineer

With the proper skills and knowledge, you can launch or advance a rewarding career in data engineering. Many data engineers have a computer science, data analytics, software engineering, or business intelligence background. Earning a degree can help build the solid quantitative foundation needed to master data and infrastructure tasks in this quickly evolving field.

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It is also popular to earn a master’s degree or higher graduate degree when pursuing a career in data engineering, and holding a postgraduate degree is likely to open opportunities to advance your career and unlock potentially higher-paying positions.

Besides earning a degree, you can take several other steps to set yourself up for success.

1. Develop your data engineering skills. 

Learn the fundamentals of data management, integration, modelling, testing, and engineering to increase your chances of success in a career in data science. Several technical skills to consider honing your skills in include:

  • Coding: Proficiency in coding languages is essential to this role, so consider taking courses to learn and practise your skills. Common programming languages include SQL, NoSQL, Python, Java, R, and Scala.

  • Relational and non-relational databases: Databases rank amongst the most common solutions for data storage. You should be familiar with both relational and non-relational databases, and how they work.

  • ETL (extract, transform, and load) systems: ETL is the process by which you’ll move data from databases and other sources into a single repository, like a data warehouse. Common ETL tools include Xplenty, Stitch, Alooma, and Talend.

  • Data storage: Some types of data should be stored differently, especially in big data. As you design data solutions for a company, you’ll want to know when to use a data lake versus a data warehouse, for example.

  • Automation and scripting. Automation is a necessary part of working with big data simply because organisations can collect so much information. You should be able to write scripts to automate repetitive tasks.

  • Data analytics and business intelligence systems: Implementing operational system data flows.

  • Machine learning: While machine learning is more the concern of data scientists, it can be helpful to grasp the basic concepts better to understand the needs of data scientists on your team. 

  • Big data tools: Data engineers don’t just work with regular data. They’re often tasked with managing big data. Tools and technologies are evolving and vary by company, but some popular ones include Hadoop, MongoDB, and Kafka.

  • Cloud computing: You’ll need to understand cloud storage and cloud computing as companies increasingly trade physical servers for cloud services. 

  • Data security: While some companies might have dedicated data security teams, many data engineers are still tasked with securely managing and storing data to protect it from loss or theft.

  • Presenting findings to non-technical audiences: Be able to explain what you are designing or fixing and how this will benefit the organisation.

2. Get certified. 

A certification can validate your skills to potential employers, and preparing for a certification exam is an excellent way to develop your skills and knowledge. Options include internationally recognised professional certifications such as:

  • Amazon Web Services (AWS) Certified Data Analytics – Specialty 

  • Cloudera Certified Associate (CCA) Spark and Hadoop Developer 

  • Associate Big Data Engineer

  • Cloudera Certified Professional Data Engineer

  • IBM Cloud Professional Architect

  • Google Certified Professional Data Engineer

  • SAS Certified Data Integration Developer 

Check out some job listings for roles you may want to apply for. If you notice a particular certification is frequently listed as required or recommended, that might be an excellent place to start.

3. Build a portfolio of data engineering projects. 

A portfolio is often a key component in a job search, showing recruiters, hiring managers, and potential employers what you can do.

You can add data engineering projects you've completed independently or as part of coursework to a portfolio website. Alternatively, post your work to the Projects section of your LinkedIn profile or to a site like GitHub—both free alternatives to a standalone portfolio site.

4. Start with an entry-level position. 

Many data engineers start in entry-level roles to build the experience and skills needed for more advanced roles. The most common roles transitioning into data engineering in the UK include software engineers, data analysts, and business intelligence developers. As you build knowledge and learn from other experts in the field, you will be more equipped to grow within your role and transition into more advanced data careers. 

Next steps

Whether you’re just getting started or looking to pivot to a new career, start building job-ready skills for roles in data with the Google Data Analytics Professional Certificate.

In under six months, you’ll learn how to perform day-to-day job responsibilities as an entry-level data analyst, key analytical skills, data management, and organisational techniques, and how to prepare yourself best to enter an exciting new role in this field. 

Article sources


LinkedIn. “LinkedIn Jobs on the Rise 2023: The 25 UK roles that are growing in demand, https://www.linkedin.com/pulse/linkedin-jobs-rise-2023-25-uk-roles-growing-demand-linkedin-news-uk/” Accessed 28th August 2023.

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