Data Engineering is software engineering applied to the movement and processing of Data. A data engineer’s role is to build applications which connect systems, collect data and process that data into a useful format for others in the business. Often this data is ‘big’ and requires distributed frameworks or pipelines to move and process it. A data engineer is a key cog in a data platform as they look after the acquisition of data for analytics, and connecting the output of ML models to operational systems.
As a Data Engineer I work with building systems that can process large amounts of data in a secure and reliable way. What I like most about the role is that I get to solve challenging problems in collaboration with other people, and that there are always new and better ways of doing things as the technologies within data and AI are evolving very quickly.
Johanna, Senior Data Engineer
BI Development & Analytics
Business intelligence (BI) is a set of technologies and practices that transform business information into actionable insights which eliminate inefficiencies and drive change.
A BI Analyst is responsible for designing, building and maintaining analytics solutions, with a primary focus on business unit-specific data marts, self-service data models, and building front-end analytical visualisations and dashboards. Whereas the BI Developer is responsible for designing, building, and maintaining end-to-end business intelligence solutions, with a primary focus on enterprise-wide data warehouse design, data models, and storage optimisation.
As a BI Developer, I help companies build, end to end BI solutions from developing data warehouses to building reports while helping companies understand valuable insights. I really enjoy helping companies reach their potential through data. I'd recommend this career to anyone wanting to build impactful solutions that make a difference.
David, Senior BI Developer
DevOps is all about the unification and automation of processes, to balance needs throughout the software development life cycle. DevOps engineers are instrumental in combining code, enabling deployment, application maintenance, and application management. Successful DevOps requires not just an understanding of development life cycles but also DevOps philosophy, practices and tools.
As a DevOps engineer, I bridge the gap between the work produced by our data/development teams and the Cloud to make the code runnable by us, the client and in some cases the public. A key part of the job is the creation of pipelines to allow code to be built, tested and deployed automatically, ensuring that quality is baked into the final product. I particularly enjoy the automation aspect of the work; watching code and infrastructure build itself is always satisfying!
Alex, Senior DevOps Engineer
Machine Learning Engineering
Combining software engineering and data science, this branch of AI involves the creation of programmes and algorithms to enable machines to act without being directed, and to learn and improve from data. An ML engineer designs and builds machine learning solutions that are reliable, fair and maintainable.
As a Machine Learning Engineer, I use scientific principles, tools, and techniques of machine learning and traditional software engineering to design and build complex computing systems. I really enjoy moving the data science lifecycle into a production system which heavily uses automation, orchestration and simplicity to generate value for the consumers. This role fits well for problem-solvers, engineers and machine learning enthusiasts who care about scale, robustness and impact.
Felix, ML Engineer