Research Scientist II, S1-4

Duties/Responsibilities

• Serve as the Task Lead for cloud data ingestion activities by planning, coordinating, and overseeing the integration, validation, optimization, and continuous improvement of data pipelines from multiple sources, ensuring data quality, system performance, and compliance with organizational standards while supporting analytics, reporting, and operational requirements
• Serve as team lead and manage data quality control and troubleshooting efforts by overseeing routine quality assurance reviews of metadata records, identifying and resolving data discrepancies, collaborating with data producers to implement corrective actions, and ensuring data integrity, consistency, and adherence to established standards
• Manage and oversee the data publication and documentation process by coordinating the ingestion, validation, publication, and maintenance of metadata records, ensuring accurate documentation, data integrity, compliance with organizational standards, and the timely availability of information for stakeholders
• Lead and oversee the design, development, testing, deployment, and maintenance of scripts and software solutions that automate business and operational processes, ensuring reliability, scalability, system performance, and adherence to organizational standards

Minimum Requirements

• Master's Degree in a relevant scientific field or an equivalent combination of education and years of work experience (preferably in science, engineering, business, or equivalent)
• 1 to 2 years of full-time verifiable work experience
• Proficiency in Python or other programming languages
• Familiarity with common scientific data formats, such as gridded data like NetCDF or HDF5
• Ability to document workflows clearly and communicate technical concepts to both scientific and engineering team members
• Knowledge of MS Office Suite

Desired Qualifications

• Foundational knowledge or hands-on experience with Amazon Web Services (AWS) environments (e.g., S3 storage, EC2, Lambda)
• Experience with GIS software (ArcGIS, QCIS) or geospatial libraries (GDAL/OGR, Rasterio, Geospandas)
• Organizational skills and an eye for detail, essential for catching inconsistencies in vast datasets
• Familiarity with Git and GitHub for code sharing and collaboration