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In the age of big data, companies collect and process vast quantities of data relating to their customers and internal processes. This data is usually scattered and unstructured, and therefore hard to analyse.

Data engineering is the process of building data pipelines to clean, store and organise that data in a structured format and making it accessible to data scientists and other teams within the organisation. These pipelines are subject to throughput, cost and scalability constraints.

Data science is the application of statistical analysis to extract insights from data to optimise processes and inform business decisions.

As the volume of data collected and stored explodes, so does the number of companies looking to gain a competitive advantage by leveraging that data to operate in a data driven manner.

Machine Learning Engineering is the engineering of software that run machine learning algorithms at scale.

Many enterprises have data science teams extracting insights from data and building machine learning models. These models need to be deployed, monitored, and integrated into the rest of the existing software infrastructure to deliver value to customers and optimise business processes. That work is done by machine learning engineers (ML engineers).