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60 Tetrascience Lead Data Architect Jobs Hiring Near You

TetraScience

Data & Semantic Model Architect

$65.25 - $84/hr

About TetraScience TetraScience is the Scientific Data and AI Company building Tetra OS, the ... The Role The Data & Semantic Model Architect will serve as the technical and strategic anchor for ...

TetraScience

Scientific Data Architect - Boston

Cambridge, MA · On-site

$69.75 - $89.50/hr

About TetraScience TetraScience is the Scientific Data and AI Company building Tetra OS, the ... ID through lead optimization), preclinical development, CMC (all drug modalities), or product ...

TetraScience

Scientific Data Architect

Waltham, MA · On-site +1

$68.75 - $88.50/hr

About TetraScience TetraScience is the Scientific Data and AI Company building Tetra OS, the ... ID through lead optimization), preclinical development, CMC (all drug modalities), or product ...

TetraScience

Scientific Data Architect

Waltham, MA · On-site

$68.75 - $88.50/hr

About TetraScience TetraScience is the Scientific Data and AI Company building Tetra OS, the ... ID through lead optimization), preclinical development, CMC (all drug modalities), or product ...

TetraScience

Scientific Data Architect

Waltham, MA · On-site

$68.75 - $88.50/hr

About TetraScience TetraScience is the Scientific Data and AI Company building Tetra OS, the ... ID through lead optimization), preclinical development, CMC (all drug modalities), or product ...

TetraScience

Scientific Data Architect - Boston

Boston, MA · On-site

$69.25 - $89/hr

About TetraScience TetraScience is the Scientific Data and AI Company building Tetra OS, the ... ID through lead optimization), preclinical development, CMC (all drug modalities), or product ...

TetraScience

Scientific Data Architect - Boston

Boston, MA · On-site

$69.25 - $89/hr

About TetraScience TetraScience is the Scientific Data and AI Company building Tetra OS, the ... ID through lead optimization), preclinical development, CMC (all drug modalities), or product ...

TetraScience

Scientific Data Architect - Boston

Boston, MA · On-site +1

$69.25 - $89/hr

About TetraScience TetraScience is the Scientific Data and AI Company building Tetra OS, the ... ID through lead optimization), preclinical development, CMC (all drug modalities), or product ...

TetraScience

Scientific Data Architect - Boston

Cambridge, MA · On-site +1

$69.75 - $89.50/hr

About TetraScience TetraScience is the Scientific Data and AI Company building Tetra OS, the ... ID through lead optimization), preclinical development, CMC (all drug modalities), or product ...

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TetraScience Jobs Information

What are the key skills and qualifications needed to thrive as a Lead Data Architect, and why are they important?

To thrive as a Lead Data Architect, you need deep expertise in data modeling, database design, and data governance, typically supported by a degree in computer science or a related field. Mastery of tools like SQL, NoSQL databases, ETL platforms, and cloud data services (e.g., AWS, Azure, GCP), along with certifications such as CDMP or AWS Certified Data Analytics, is highly valued. Strong leadership, communication, and problem-solving abilities help you guide teams and collaborate with stakeholders effectively. These competencies ensure robust, scalable data architectures that support organizational goals and reliable decision-making.

How does a Lead Data Architect typically collaborate with other IT and business teams during large-scale data projects?

A Lead Data Architect works closely with cross-functional teams, including data engineers, business analysts, and stakeholders, to ensure that data solutions align with organizational goals. They often facilitate discussions to translate business requirements into technical specifications and establish data governance standards. Regular collaboration is essential for designing scalable data architectures, troubleshooting integration challenges, and ensuring data security. This role also involves mentoring junior team members and coordinating with project managers to deliver projects on time.

What does a Lead Data Architect do?

A Lead Data Architect is responsible for designing, implementing, and managing an organization’s data architecture. This includes creating data models, defining data flows, and ensuring that data is stored securely and efficiently across systems. They work closely with stakeholders to align data solutions with business goals and oversee the work of other data professionals. Additionally, they ensure data governance and compliance with relevant regulations.

How much money do data architects make?

Data architects typically earn a median annual salary between $100,000 and $150,000, depending on experience, location, and industry. Senior roles with advanced skills in cloud platforms and data modeling can exceed $160,000 annually.

What is the difference between Lead Data Architect vs Data Engineer?

AspectLead Data ArchitectData Engineer
Required CredentialsBachelor's or Master's in Computer Science, Data Science, or related field; certifications like AWS Certified Data Analytics or Microsoft Certified: Azure Data EngineerBachelor's in Computer Science, Software Engineering, or related; certifications like Google Cloud Professional Data Engineer or similar
Work EnvironmentDesigns data architecture, oversees data strategy, collaborates with stakeholders, often in leadership rolesBuilds, maintains, and optimizes data pipelines and infrastructure, working closely with data teams
Employer & Industry UsageUsed across industries for data strategy and architecture planningCommonly employed in data engineering teams for data processing and pipeline development

The main difference is that a Lead Data Architect focuses on designing and overseeing the overall data architecture and strategy, while a Data Engineer implements and maintains the data pipelines and infrastructure. Both roles require similar credentials but serve different functions within data teams.

What other companies are hiring for Lead Data Architect jobs?
What are the most popular jobs at Tetrascience?
What are the most popular categories at Tetrascience?
Infographic showing various Lead Data Architect job openings at Tetrascience in the United States as of May 2026, with employment types broken down into 100% Full Time. Highlights an 76% Physical, 2% Hybrid, and 22% Remote job distribution.
Data & Semantic Model Architect

Data & Semantic Model Architect

TetraScience

Remote

$65.25 - $84/hr

Full-time

Posted 15 days ago

Job description

About TetraScience
TetraScience is the Scientific Data and AI Company building Tetra OS, the operating system for scientific intelligence. We help the world's leading life sciences firms turn fragmented scientific data into AI-native assets and scientific workflows that accelerate discovery, development, and manufacturing. TetraScience's growing ecosystem of strategic partners includes NVIDIA, Databricks, Thermo Fisher Scientific, Snowflake, Google, and Microsoft.
In connection with your candidacy, you will be asked to carefully review "The Tetra Way," authored by our CEO, Patrick Grady; it is impossible to overstate the importance of this document, and you should take it literally as you decide whether our mission, culture, and expectations are right for you.
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 all"-bridging deep technical semantics, system architecture, and business outcomes.
Crucially, you will be the owner of the Common Model & Exchange Layer of our platform: a set of unified, reusable common data models that allow data to flow seamlessly across different customer environments while driving towards true Ontology. You will define the data contracts and consistent definitions that empower our Forward 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 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, knowing they are building on a stable, consistent semantic foundation.
  • 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.

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 complex 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 Contracts & Governance: Establish the "rules of the road" for data quality and consistency. Define how data contracts are versioned, enforced, and evolved, ensuring that downstream 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 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. You understand the challenges of mapping heterogeneous source data into a single, unified target schema.
  • Data Contract Design: Experience defining and enforcing data contracts 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 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. 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 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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