Data engineers create data pipelines for organizational analysis. Candidates are responsible for extracting and transferring big data, then improving and presenting that information to data scientists and other team members within the organization. They solve problems with data integration, and must often work with unstructured data sets to make them easier to understand. They help develop and construct databases and large-scale data processing systems, and test and maintain these systems for proper functionality. Data engineers deal with data that may contain errors, is unformatted and may contain system-specific codes. A successful data engineer recommends and implements systems that improve the quality, efficiency and reliability of the data systems, and ensures that the architecture meets the needs of the organization. A qualified candidate will have a keen knowledge of industry standards, and experience working with SQL, Python, Hadoop and similar technologies. Data engineers must work closely with analytics and data experts to maximize the functionality of individual data systems, and improve information mining processes.
With a keen knowledge of the processes required for improving data quality, data engineers are key members of data analytics teams. IBM, in partnership with the Business-Higher Education Forum and Burning Glass Technologies, published a report predicting that the demand for data engineers will grow 39% by 2020, with 59% of the available jobs being in the IT, professional services, insurance and finance sectors. With their abilities to make big data more accessible and understandable, people in this field can expect a wealth of opportunities with both small and large organizations.
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