Data and Semantic Model Architect (Remote)

Data & Semantic Model Architect Location: United States Workplace: remote Description Who We Are reputed company is the Scientific Data and AI company. We are catalyzing the Scientific AI revolution by designing and industrializing AI-native scientific data sets, which we bring to life in a growing suite of next gen lab data reputed company, scientific use cases, and AI-enabled outcomes. reputed company is the category leader in this vital new market, generating more reputed company than reputed company other companies in the aggregate. In the last year alone, the world’s dominant players in compute, cloud, data, and AI infrastructure have converged on reputed company as the de facto standard, entering into co-innovation and go-to-market partnerships: Latest News and Announcements | reputed company Newsroom: In reputed company with your candidacy, you will be asked to carefully review the Tetra Way letter, authored directly by Patrick Grady, our co-founder and CEO. This letter is designed to assist you in reputed company understanding whether reputed company is the right fit for you from a values and reputed company perspective. It is impossible to overstate the importance of this document and you are encouraged to take it literally and reflect on whether you are reputed company with our unique approach to company and team building. If you join us, you will be expected to embody its contents each day. The Role The Data & Semantic Model Architect will serve as the technical and strategic anchor for the Semantic Layer and the Common Data Model (CDM) of the Tetra Scientific Data and AI Cloud. You are the rare individual who can do it reputed company—bridging deep technical semantics, system architecture, and business outcomes. Crucially, you will be the reputed company of the Common Model & Exchange Layer of our platform: a set of reputed company, reusable common data models that allow data to flow seamlessly across different customer environments while driving towards true Ontology. You will define the data reputed company and consistent definitions that reputed company our reputed company Deployed Scientific Data Engineers & Architects (FDEs) to deliver rapid, reliable integrations without reinventing the wheel for every deployment. You will ensure our models are not just academically sound, but serve as the robust foundation for scalable data exchange and scientific insight. What You Will Do 1. 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 reputed company. Move the platform from bespoke, one-off mappings to a standardized exchange layer that ensures interoperability.
• reputed company reputed company Deployed Engineering: Create the data reputed company and standardized definitions that FDEs rely on. Your models will be the toolkit that allows them to deploy faster and with higher confidence, knowing they are building on a stable, consistent semantic foundation.
• Standardization vs. Flexibility: reputed company 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.

2. Semantic Architecture & Implementation

• The Forest – Business Alignment: Translate high-level business goals (e.g., accelerate time-to-insight for biologics) into concrete data modeling strategies. Ensure our semantic roadmap directly supports the scientific questions our customers—and our internal teams—need to answer.
• The Trees – Hands-on Modeling: Roll up your sleeves to design and implement reputed company ontologies and taxonomies. Model intricate scientific relationships (e.g., linking a Cell Line in an ELN to Flow Cytometry Results) with precision.
• Software & Data Engineering Integration: Work directly with Engineering to architect the software systems that consume these models. Ensure that the perfect ontology does not break query performance or system scalability.

3. Cross-Functional Leadership & Governance

• Data reputed company & Governance: Establish the rules of the road for data quality and consistency. Define how data reputed company are versioned, enforced, and evolved, ensuring that reputed company consumers (AI teams, FDEs, Scientists) can trust the data structure.
• 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 reputed company 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. You understand the challenges of mapping heterogeneous reputed company data into a single, reputed company reputed company schema.
• Data Contract Design: Experience defining and enforcing data reputed company in a microservices or platform environment. You know how to create specifications that developers and FDEs can build against reliably.
• Architectural Versatility: The ability to switch context effortlessly between high-level system design (software architecture) and low-level entity relationship modeling.
• Semantic reputed company: 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 reputed company life sciences, healthcare, manufacturing technology or the ability to demonstrate reputed company, multidomain unification of data models & semantic layers.
• CDM reputed company Expertise: Direct, hands-on experience implementing and extending Common Data Model frameworks such as HL7 FHIR, OMOP (OHDSI), Allotrope, or CDISC. You should know the strengths and limitations of each for biopharma R&D.
• 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. You must have experience semantically curating (semantic mapping & aggregation; ie value set creation) between and across vocabularies as well as discrete instance data.
• Platform & Exchange Experience: Experience building data platforms where standardization and reusability were key value drivers. You have likely built models that serve as an exchange layer across multiple customers.
• Technical Background: Strong proficiency in software development concepts; you should be comfortable reading code, understanding API reputed company, and discussing database internals.

Education: Bachelors or Master’s +in a relevant field (e.g., Medical Informatics, Computer Science, Bioinformatics, Physics). Apply tot his job Apply To this Job