Data engineering is a rapidly growing specialism in the tech sector concerned with collecting, accessing, and storing raw data. They use their engineering knowledge to build systems to ensure this raw data can be easily analysed – typically by data scientists or business analysts.

Think of data engineers as sorters, organising the chaos and mess of large amounts of data into streams and pipelines so that it becomes useful to the end user.

The Data Engineer Role

As with most roles, the day-to-day tasks of a data engineer can vary from company to company, but generally, these are some of the everyday tasks they are expected to carry out:

·  Building and testing databases

·  Keeping pipelines and databases maintained

·  Creating algorithms to sort data

·  Regularly collaborating with data scientists and business analysts to create the solutions they need

·  Creating internal business intelligence reports based on data

·  Integrating new datasets into data pipelines

What data engineers do with data and why they do it will very much depend on not only the size of the company they are working at but also the seniority of their role. For example, data engineers are essential for machine learning and deep learning as they become more senior in their position and transition into one of those niches.

Skills Required to Become a Data Engineer

Because of the technical skills required for the role, data engineering can sometimes be confused with software engineering. However, it is an entirely separate role and is considered a specialist area within software engineering. Speaking of technical skills, some of the ones you’re likely to need if you choose to become a data engineer are Python, SQL, and Scala. Because data engineering requires some of the same skills as back-end engineering, you may often find people switching from one to the other early in their careers.

Once you get further on in your career as a data engineer, the most popular way to explain how the role changes is to split it into these commonly-used categories: generalist, pipeline-centric, and database-centric.

• Generalist: Someone working at a small company or as part of a smaller team. Will likely have sole responsibility for the entire process from data collection to management and analysis.
• Pipeline-centric: A data engineer who’s likely working in a mid-size organisation. Will probably collaborate with a data scientist to ensure that the data collected is of use.
• Database-centric: Likely to be found in larger organisations where they are entirely in charge of data flow. Unlike pipeline-centric data engineers, they will instead focus on working with analytics databases. 

Becoming a Data Engineer

Like most tech careers these days, starting your journey as a data engineer no longer requires you to have a university degree, making the specialism far more accessible. A 2022 survey found that 1 in 3 engineers didn’t go to university to learn their coding skills. Instead, you can opt for a much shorter choice like a bootcamp. Although intense, you will gain all the skills you need to become a junior data engineer in just three months. 

As you would expect, becoming a data engineer can prove a very lucrative career. This is particularly true at the moment as the specialism has once again made an appearance on LinkedIn’s yearly Jobs on the Rise list. After 4-9 years of experience, you could earn an available salary of £48,400 as a mid-level data engineer. Entry-level salaries look no less enticing, with an average of £33,700 on the table.