Data & Semantic Model Architect
T
TetraScienceLife sciences, healthcare, manufacturing technology
Workable remote: True
Workable locations: United States
Location: United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 7+ years of experience in data architecture, informatics, or technical product leadership
- Required Skills
- Software Development
Requirements
- 7+ years of experience in data architecture, informatics, or technical product leadership, specifically within life sciences, healthcare, manufacturing technology or the ability to demonstrate complex, multidomain unification of data models & semantic layers.
- Direct, hands-on experience implementing and extending Common Data Model frameworks such as HL7 FHIR, OMOP (OHDSI), Allotrope, or CDISC.
- Proven mastery in standardizing messy, heterogeneous data using both standard vocabularies (such as terminology standards & ontologies) as well as proprietary or custom vocabularies.
- Experience semantically curating (semantic mapping & aggregation; ie value set creation) between and across vocabularies as well as discrete instance data.
- Experience building data platforms where standardization and reusability were key value drivers.
- Strong proficiency in software development concepts; comfortable reading code, understanding API contracts, and discussing database internals.
- Bachelor's or Master’s in a relevant field (e.g., Medical Informatics, Computer Science, Bioinformatics, Physics).
- Proven ability to design shared data models that serve as an exchange format between different systems or organizations.
- Experience defining and enforcing data contracts in a microservices or platform environment.
- Ability to switch context effortlessly between high-level system design (software architecture) and low-level entity relationship modeling.
- Deep, hands-on expertise with semantic web standards (RDF, OWL, SHACL, SPARQL) and property graph concepts (LPG).
Responsibilities
- Architect the Exchange Layer: Design and own the Common Data Models (CDMs) that serve as the universal language for scientific data across our customer base.
- Empower Forward Deployed Engineering: Create the data contracts and standardized definitions that FDEs rely on.
- Standardization vs. Flexibility: Strike the strategic balance between rigid global standards (for cross-customer exchange) and local flexibility.
- The "Forest" – Business Alignment: Translate high-level business goals into concrete data modeling strategies.
- The "Trees" – Hands-on Modeling: Roll up your sleeves to design and implement complex ontologies and taxonomies.
- Software & Data Engineering Integration: Work directly with Engineering to architect the software systems that consume these models.
- Data Contracts & Governance: Establish the "rules of the road" for data quality and consistency.
- Scientific Translation: Partner with Scientific Business Analysts to decode the complexity of biopharma R&D.
- Interoperability: Architect models that ensure our data is FAIR and ready for downstream AI/ML applications.
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