Data engineers work on a wide range of data-related projects. They take on projects ranging from implementing data clean-up processes on existing systems to deploying entirely new data architectures.
The people in this discipline are often the bridge between data scientists and developers. They are the ones who understand the ins and outs of how data is stored, accessed and processed, becoming a crucial resource for the data scientists, providing the bridges between the data analysts and the programmers. Their main goal designing and implement data pipelines. A data pipeline is a designed collection of processes and tools used to process and analyze data.
They are often part of an organization’s data science or business intelligence teams, although they can be just as valued in positions like data architect or data analyst.
Data engineers typically need a bachelor’s degree in computer science, statistics, or a related field. While a computer science degree isn’t required, it’s helpful, because it provides a foundational knowledge of programming, data structures, algorithms and computer architecture.
Usually employed by large companies, they can be useful in some fields that require special data treatment.
The data universe is expanding rapidly and data science is such a broad field that job titles are often misleading. Even if we only focus on data scientists, the possibilities are endless. These professionals need skills in statistics, computer science, and mathematics. But they also need communication skills, because it is a relatively young field, there isn’t a set procedure for becoming a data science professional. People who want to become data scientists usually start by working toward a computer science or statistics degree.