# Data & Semantic Model Architect

**Company:** [TetraScience](https://hotfix.jobs/companies/tetrascience)
**Location:** Remote
**Role:** Data Engineering
**Experience:** 7+ years
**Skills:** Hl7 Fhir, Omop, Allotrope, Cdisc, Rdf, Owl, Shacl, Sparql, Semantic Web, Property Graph, Data Modeling, Ontologies, Taxonomies, Data Contracts, Common Data Model
**Posted:** 2026-05-11

> Designs and owns Common Data Models and semantic layers for scientific data interoperability in life sciences. Translates business goals into ontologies, ensures FAIR data for AI/ML, and empowers forward-deployed engineers with standardized contracts. Requires 7+ years in data architecture and CDM expertise like HL7 FHIR, OMOP.

## Job Description

## Responsibilities

### Common Data Model & Exchange Strategy
- 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. Move the platform from bespoke, one-off mappings to a standardized \"exchange layer\" that ensures interoperability.
- Empower Forward Deployed Engineering: Create the data contracts and standardized definitions that FDEs rely on. Your models will be the toolkit that allows them to deploy faster and with higher confidence.
- Standardization vs. Flexibility: Strike the strategic balance between rigid global standards (for cross-customer exchange) and local flexibility. Define the core \"immutable\" aspects of the model versus where extension is permitted.

### Semantic Architecture & Implementation
- The \"Forest\" – Business Alignment: Translate high-level business goals into concrete data modeling strategies. Ensure our semantic roadmap directly supports the scientific questions our customers need to answer.
- The \"Trees\" – Hands-on Modeling: Design and implement complex ontologies and taxonomies. Model intricate scientific relationships with precision.
- Software & Data Engineering Integration: Work directly with Engineering to architect the software systems that consume these models. Ensure that the ontology does not break query performance or system scalability.

### Cross-Functional Leadership & Governance
- Data Contracts & Governance: Establish the \"rules of the road\" for data quality and consistency. Define how data contracts are versioned, enforced, and evolved.
- Scientific Translation: Partner with Scientific Business Analysts to decode the complexity of biopharma R&D. Turn ambiguous scientific requirements into rigorous, machine-readable data structures.
- Interoperability: Architect models that ensure our data is FAIR (Findable, Accessible, Interoperable, Reusable) and ready for downstream AI/ML applications.

## Skills & Competencies
**Common Data Model Expertise**: Proven ability to design shared data models that serve as an exchange format between different systems or organizations.
**Data Contract Design**: Experience defining and enforcing data contracts in a microservices or platform environment.
**Architectural Versatility**: The ability to switch context effortlessly between high-level system design and low-level entity relationship modeling.
**Semantic Fluency**: Deep, hands-on expertise with semantic web standards (RDF, OWL, SHACL, SPARQL) and property graph concepts (LPG).

## 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.
- CDM Framework Expertise: Direct, hands-on experience implementing and extending Common Data Model frameworks such as **HL7 FHIR**, **OMOP (OHDSI)**, **Allotrope**, or **CDISC**.
- Terminology & Standardization: Proven mastery in standardizing messy, heterogeneous data using both standard vocabularies (such as terminology standards & ontologies) as well as proprietary or custom vocabularies.
- Platform & Exchange Experience: Experience building data platforms where standardization and reusability were key value drivers.
- Technical Background: Strong proficiency in software development concepts; comfortable reading code, understanding API contracts, and discussing database internals.
- Education: Bachelor's or Master’s in a relevant field (e.g., Medical Informatics, Computer Science, Bioinformatics, Physics).">

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