Geospatial Data Engineer II

Major Duties/Responsibilities

• R&D: assist in conducting research and development to support project-specific applications.
• Data Development: working alongside researchers to develop and deploy geospatial data storage and processing solutions.
• Requirements Decomposition: working alongside researchers and project sponsors to capture, understand, integrate, and implement project requirements in developed system architectures and software.
• Interdisciplinary Collaboration: collaborating with a highly diverse and multidisciplinary team – from photogrammetrists, geographers, mathematicians, physicists, computer scientists, and engineers – in research, development, integration, testing, and deployment.

Basic Qualifications

• Requires an B.S. or M.S. in the field of Geography, Data Science, Computer Science, or related fields with a minimum of 2 years of relevant experience
• Experience with programming languages such as SQL, Python
• Experience with Extract, Transform, Load (ETL) processes.
• Excellent written and oral communication skills and the ability to communicate in English to a scientific audience
• Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to changing needs
• Demonstrated ability to present technical results to technical communities.
• Excellent interpersonal skills with a strong commitment to a team environment

Preferred Qualifications

• Experience with Docker, Kubernetes, or similar container platforms/concepts.
• Experience with s3, parquet, Duckdb, zarr and related software and technologies is a plus
• At least 2 years of experience working with geospatial data and processing workflows and assessing data sources for fit-for-purpose.
• Motivated self-starter with the ability to work independently and to participate creatively in collaborative and frequently interacting teams of researchers.
• Experience with software and data governance best practices including, but not limited to: Agile development; version control using Git/GitFlow or similar system; data retention life-cycles, and project management via systems like JIRA, Asana, etc.
• Utilize open-source and commercial tools and approaches to solve complex problems including information retrieval/extraction, machine learning/deep learning, and networking