Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data at scale. It involves the development, implementation, and maintenance of systems and processes that take in raw data and produce high-quality, consistent information that supports downstream use cases, such as analysis and machine learning. Data engineers are responsible for laying the foundations for the acquisition, storage, transformation, and management of data in an organization. They manage the design, creation, and maintenance of database architecture and data processing systems, ensuring that the subsequent work of analysis, visualization, and machine learning models development can be carried out seamlessly, continuously, securely, and effectively.
Some common tasks that data engineers perform include acquiring datasets that align with business needs, cleaning and transforming data, building and maintaining data pipelines, and ensuring data quality and security. They are focused on the production readiness of data and things like formats, resilience, scaling, and security. Data engineers work in a variety of settings to build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret.
Data engineering is a broad field with applications in just about every industry. As long as there is data to process, data engineers will be in demand. In fact, data engineering is one of the most in-demand positions in the data science industry.
