I and others are writing more and more about 'data engineering' but in most circles it's a term without an exact definition. Simply put, data engineering facilitates better flow and access to the data within the teams in your organization. It gives you the ability to collect, clean, store and manipulate your data and make it readily available for analysis.
Most companies have multiple data sources and collect their data in a variety of formats, such as text files, database logs, multimedia files, etc. Data engineers build and maintain the data infrastructure that allows for collection and storage of this data. They are also responsible for building a system that cleans and transforms this data into a format that data scientists can then use to generate valuable insights. This involves creating optimal databases, defining and implementing schema changes, handling the metadata, and integrating new data management tools and systems.
Data engineering also entails some critical tasks that ensure smooth and efficient functioning of your data pipeline. Some of these key tasks include workflow scheduling, autoscaling to handle traffic spikes and, most importantly, building a robust infrastructure that operates seamlessly for months or even years - with minimal upgrades and tweaking.