Metadata & Submission Agreement Analyst

CSS, Inc

About The Position

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.

Requirements

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

Nice To Haves

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 data readiness, data governance, or digital stewardship programs

Responsibilities

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.
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.
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.
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.
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.