OISS Workflow Developer

This position provides expert metadata analysis in support of Artificial Intelligence Contexts (AICs) and Artificial Intelligence Units (AIUs) by reviewing Submission Agreements (SAs) and identifying the essential contextual, structural, and stewardship fields required for artificial-intelligence-ready data ingestion, governance, discovery, and reuse. The role focuses on translating contractual, scientific, and operational requirements documented in Submission Agreements into standardized, machine-actionable metadata elements that support the full data lifecycle, provenance, transparency, and responsible artificial intelligence use. **Key Responsibilities** **Submission Agreement & Metadata Analysis** - Review Submission Agreements to identify required and implied metadata fields, including: - Data types, variables, and products - Temporal resolution, coverage, and frequency - Spatial references, station identifiers, and observing platforms - Data status (e.g., preliminary, verified, or derived) - Submission schedules, update cadence, and lifecycle expectations - Interpret responsibilities of the data provider and long-term archive to ensure metadata accurately reflects stewardship, preservation, and access obligations. **Artificial Intelligence Context (AIC) & Artificial Intelligence Unit (AIU) Definition** - Define the minimum essential metadata fields required to establish: - Artificial Intelligence Contexts (AICs): descriptive context that defines how data can be interpreted, constrained, and responsibly used by artificial intelligence or machine learning systems - Artificial Intelligence Units (AIUs): discrete, machine-actionable data units with clearly defined provenance, structure, and meaning - Map Submission Agreement-derived metadata fields to appropriate internal schemas and external metadata standards. **Provenance, Lineage, and Context Modeling** - Capture and document data lineage elements such as: - Observing system or scientific program - Processing level and verification status - Responsible organizations and points of contact - Ensure clear distinctions between raw observations, processed products, and derived datasets are represented in metadata definitions. **Collaboration & Documentation** - Collaborate with data stewards, archivists, software engineers, and artificial intelligence or machine learning teams to ensure metadata is: - Scientifically accurate - Contractually compliant - Scalable and reusable across systems - Produce clear documentation, metadata field inventories, and crosswalks to support implementation and review. **Professional Accountability & Team Engagement** - Attend and actively participate in team meetings, technical discussions, and coordination sessions. - Commit to and complete assigned work tickets by agreed-upon due dates. - Proactively communicate risks, blockers, or dependencies that may impact delivery, including a clear explanation and proposed mitigation or revised timeline. - Provide clear, accurate, and detailed weekly status reports that include: - Work completed during the reporting period - Current status of assigned tickets - Identified risks or issues - Planned work for the upcoming reporting period - Ensure reported status aligns with actual progress to support management monitoring and program oversight. **Required Skills & Experience** - Experience performing scientific or technical metadata analysis - Ability to interpret formal agreements, technical documentation, and governance artifacts - Strong understanding of data lifecycle concepts (data acquisition, verification, archival, and access) - Excellent written communication and documentation skills **Desired Qualifications** - Experience with environmental, oceanographic, meteorological, or geophysical data - Familiarity with common metadata standards (e.g., International Organization for Standardization 19115, Directory Interchange Format, Climate and Forecast conventions) - Exposure to artificial intelligence…