A combination of education, training and experience may be required to become a data modeler. Although it is an information technology career, this position also requires an understanding of business issues and the ability to communicate effectively. Along with other information technology careers, a data modeler may enjoy a strong job outlook and a relatively high salary.
To become a data modeler, you may wish to consider earning an undergraduate degree in computer science or information management. Coursework related to data modeling or database design is likely to be useful. If you would like to work for a large organization, you may also want to consider a Master’s degree in one of these fields.
Along with your college degree, training in using industry standard data modeling tools may help in becoming a data modeler. Organizations that hire data modelers may prefer people who already know how to use these tools. You may also wish to obtain certifications, which can be either for data modeling concepts, or for using a specific tool. Not only may certification help you gain additional data modeler training, but it may show future employers that you are serious about your career. If you pursue certification, ensure the accrediting agency is respected in the industry.
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Some organizations, especially larger ones with complex data requirements, may prefer to hire data modelers with work experience. If you are in college, you may wish to seek an internship in this field as you prepare to become a data modeler. Working as a business analyst provide experience that will help you become a data modeler. In addition, you may find it is easier to be hired as a data modeler in a smaller organization, and then work your way to a larger organization.
Although data modeling requires a great deal of analytical ability, communication skills also are essential to success as a data modeler. Data modeler duties frequently include meetings with business users and technical staff. These meetings may involve discussing business requirements, along with the sources and uses of data for application development. The data modeler may then need to write business requirements and present them to both technical and non-technical staff for agreement and approval.