The Data Management group in Research Informatics (RX) is seeking a visionary and technically skilled Data Architect to lead the design and implementation of enterprise-scale data architectures. This role is pivotal in ensuring that biomedical research data is AI-ready, accessible, and interoperable across scientific domains. The focus is on designing robust data architectures, technical stacks, and APIs that enable seamless data integration, governance, and utilization by AI models and data scientists.

Major Responsibilities

- Design and evolve enterprise-level data architectures that support scientific data interoperability and AI-readiness.
- Define and implement technical stacks and APIs to enable secure, scalable, and performant data access.
- Establish formal data architecture processes aligned with research data management policies and FAIR principles.
- Collaborate with domain experts to define logical data domains, sub-domains, and critical data elements (CDEs).
- Inventory and map physical data repositories to logical domains and facilitate metadata capture at data creation.
- Develop conceptual and logical data models and document relationships between data objects.
- Ensure alignment with scientific standards (e.g., HELM, CDISC) and enforce metadata governance.
- Promote the creation of “born FAIR” data and support the publication of CDEs to foundational platforms (e.g., data lakes, warehouses).
- Collaborate with IT, data engineering, and security teams to ensure compliance, automation, and operational integration.
- Drive architectural improvements and standardization across scientific data workflows. ​

Essential Requirements:

- 10+ years of experience in data architecture, data engineering, or software development.
- 5+ years in scientific software or drug discovery environments.
- Master’s degree or equivalent experience in a relevant field.
- Deep expertise in designing data architectures, APIs, and technical stacks.
- Experience with RDF, ontologies, cloud platforms (AWS, Google, Azure), and graph databases.
- Strong understanding of laboratory data workflows and scientific data types (biology, chemistry, clinical).
- Proven ability to align scientific and IT stakeholders around strategic data initiatives.

This role offers a unique opportunity to shape the future of data architecture in biomedical research and to enable transformative AI-driven insights. ​
 

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