Career Category
Engineering
Job Description
Position Overview
The GCF5 Senior Data Architect is the senior technical authority for data standards, semantics, and FAIR-aligned data architecture within the Enterprise Data Foundation (EDF) / Common Data Model (CDM) pillar.
This role defines and evolves canonical data models, metadata standards, identifiers, and interoperability patterns that enable FAIR, reusable, and AI‑ready data products across research and platform domains. The Data Architect ensures that FAIR principles are embedded by design across data lifecycles, while partnering with engineering teams to enable scalable and consistent implementation.
This role does not primarily own pipeline delivery, but instead owns the architectural decisions that make pipelines reusable, intelligible, and durable over time.
The role reports to the GCF7 leader and partners closely with peer GCF5 domain and platform leads across SCIP to ensure cohesive semantic and architectural evolution.
Core Responsibilities
Data Architecture & FAIR Standards
- Define, evolve, and socialize FAIR‑aligned canonical data models, metadata schemas, identifiers, and naming conventions across domains.
- Own semantic interoperability strategies, including controlled vocabularies, reference data, and domain model alignment.
- Establish architectural patterns that ensure data products are Findable, Accessible, Interoperable, and Reusable by default.
- Define standards for versioning, provenance, lineage, and reuse contracts across the data lifecycle.
Governance & Adoption
- Partner with data governance, stewardship, and scientific domain leads to ensure standards are usable, adopted, and evolve pragmatically.
- Lead architectural review and decision forums for schema, model, and standards changes.
- Define and track FAIR maturity indicators (e.g., metadata completeness, reuse readiness, interoperability coverage).
Architecture Enablement
- Translate scientific and research workflows into durable architectural abstractions rather than bespoke implementations.
- Provide architectural guidance and reference designs to data engineering teams implementing ingestion and transformation pipelines.
- Influence tooling and platform capabilities to better support metadata, lineage, catalogs, and FAIR assessments.
Leadership & Influence
- Mentor engineers and technical leads on data modeling, semantics, and architectural reasoning.
- Act as a thought leader for FAIR data architecture across SCIP and adjacent organizations.
- Communicate architectural intent clearly through design artifacts, documentation, and standards proposals.
Core Competencies
- Deep expertise in data modeling, metadata architecture, and semantic standards, with evidence of reuse at enterprise scale.
- Strong grasp of FAIR principles and their practical application in research and analytics environments.
- Architectural systems thinking: tradeoffs across flexibility, governance, usability, and scalability.
- Ability to influence without direct ownership of delivery; strong written and verbal communication.
- Domain literacy in scientific or research data (e.g., biology, chemistry, therapeutic discovery).
Core Success Measures
- Adoption and reuse of canonical data and metadata models across domains.
- Measurable improvements in FAIR maturity (findability, interoperability, reuse readiness).
- Reduction in bespoke schemas and one‑off semantic mappings.
- Improved time‑to‑reuse for datasets across research teams.
- Positive feedback from engineers and scientists on clarity and usability of standards.
Key Relationships
- Partners with GCF5/6 Data Engineering and Platform Leads to enable implementation of standards.
- Works closely with scientific domain leads, stewards, and governance bodies.
- Interfaces with architecture, security, compliance, and vendor ecosystems on standards alignment.
Decision Authority
- Approve and evolve canonical data models, metadata standards, and architectural patterns.
- Decide when and how FAIR standards evolve, including backward compatibility and deprecation.
- Recommend tooling and platform investments to support FAIR adoption.
- Influence—but does not directly commit—engineering capacity.
Qualifications
Basic Qualifications
- BS+8 / MS+6 / PhD in CS, Data, Engineering, or scientific disciplines.
- Demonstrated experience designing enterprise‑scale data models and standards.
- Experience working with scientific or research data domains.
Preferred Qualifications
- Experience with FAIR, research data management, semantic modeling, or ontology-adjacent work.
- Familiarity with metadata catalogs, lineage systems, and data governance frameworks.
- Evidence of standard‑setting and cross‑organizational influence.
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