Data Management Fellow to the Great Lakes Observing System(GLOS) (posted 9/5/17)
The Great Lakes Observing System (GLOS), an Ann Arbor-based 501(c)3 non-profit, is seeking a Data Management Fellow to lead its product and content-related efforts. This fellow will spend 15-20 hours/week over a 9-12 month period providing the vision for how key regional oceanographic data is disseminated, overseeing operational efforts to manage Great Lakes data streams, and setting and tracking success criteria for launched products.
Key responsibilities to include:
- Assess the health of existing products, data streams, and processing pipelines.
- Set and track success metrics for GLOS products and data streams, make recommendations to board and executive director to optimize their impact.
- Consult with staff, executive director, and board to develop strategic vision for product and content development.
- Responsible for managing operations team, composed of contractors carrying out the technical vision.
- Understand data-related user needs of the Great Lakes.
- Assist executive director in developing set of next-gen product and grant proposals for addressing these gaps.
Key experience or knowledge:
- Modern database development expertise, MySQL preferred
- Analytics and statistical software: Google Analytics, R, etc
- Web Services – Experience building modern web services
- Social App Development – Experience in building social sites or apps (Facebook, etc.)
- Linux and associated tools, e.g. Bash
The candidate should have a bachelor’s degree, preferably in computer or information science. Training in business and geographical information science is a plus. Preference will be given to candidates who are currently enrolled in, or have recently completed, a graduate program. Prior experience supporting data systems in a business environment is desirable.
GLOS is a non-profit organization based in Ann Arbor, MI with a mission to facilitate broader access of Great Lakes data, the bulk of which are real-time buoy data. We manage 7.7 terabytes of data on a half rack of servers.