Data engineers are technical professionals who are skilled at handling the complexities within data. They design and build custom software to transform raw data into information or insights that can be consumed by end users.

Data engineers work as part of data analytics teams, product teams or business intelligence teams. There are mainly four types of data engineers, depending on the type of problem they are trying to solve.

Data Engineers for structured data

Data engineers for structured data are skilled in SQL and databases, and are generally deployed to build data warehouses. They are proficient in extracting, transforming and loading data into data warehouses. They are skilled in ETL tools and understand the architecture of data warehousing applications.

Data Engineers for high volume data

Data engineers for high volume data are referred to as big data engineers, and are proficient in technologies that can process and store huge volumes of data efficiently. They are skilled in big data processing technologies like bigquery and distributed data storage platforms like hadoop.

Data Engineers for streaming data

Data engineers for high velocity data are referred to as fast data engineers, and are proficient in technologies that can handle streaming data. They are deployed to build near real time or real time data driven applications. They are skilled in technologies that are used to process streaming data like spark. The processing of streaming data generally happens in memory, and only transactional commits and audit entries are persisted.

Data Engineers for unstructured data

Data engineers for unstructured data are skilled in technologies that process speech, text, images and videos. They are skilled in interpreting unstructured data and converting it into a structured structure for further processing or interpretation. They use advanced cutting edge open source technologies to process unstructured data.