Senior Data Lifecycle & Governance Architect

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We are looking for a senior cross-disciplinary Data Architect to advance research data capabilities within a national laboratory environment. This role requires deep expertise in research data management, governance, and interoperable data systems, with a focus on supporting mission-driven science across multiple facilities and programs. The successful candidate will lead the creation and refinement of data architecture and governance strategies that will inform and guide the development of AI-enabled computational science at the leading edges of computing.

The Data Engineering group at ORNL focuses on research and development for innovative, world-class data systems. We pursue internal and external opportunities to advance scientific research and national security programs through the application of our extensive expertise in the collection, management, distribution, and curation of data. We hire top researchers and engineers who are ready to tackle the hardest problems faced by both the US and the world.

As a US Department of Energy Office of Science national laboratory, ORNL has an extraordinary 80-year history of solving the nation's biggest problems. ORNL's staff of over 7000 dedicated and creative people is committed to the laboratory's broad mission to accelerate scientific discoveries and translate those discoveries into energy, environment, and security solutions for the nation.

Major

Duties / Responsibilities:

• Use data architecture and governance expertise to support projects funded by DOE ASCR, other DOE program offices, strategic partnership programs, and strategic Division and Directorate initiatives.
• Lead and coordinate multi-facility and multi-program data management and governance initiatives aligned with laboratory mission priorities.
• Apply ontology engineering and semantic technologies to support knowledge representation, data integration, and discoverability.
• Establish and promote best practices for research data lifecycle management, including metadata standards, curation, stewardship, and compliance with federal policies.
• Establish collaborations within ORNL and with other national labs, academic, government, and commercial organizations that promote the visibility and reputation of the division and ORNL.
• Support recruiting, selection, hiring and mentoring of the technical staff.
• Serve as a subject matter expert to the directorate in the areas of data engineering, data architectures, and resource utilization, as needed.
• Represent ORNL and CSMD by developing high quality papers highlighting our accomplishments, engaging with the scientific community at conferences and meetings to understand current state of data challenge, and interacting with industry partners to develop next generation capabilities.

Basic Qualifications:

• M.S. in Computer Science or related field.
• 12+ years of programing and software engineering experience in all aspects of the software development life cycle.
• 5+ years of experience in designing state-of-the-art, large-scale data science platforms and frameworks for federal, state, or commercial organizations.
• Proven ability to lead complex, cross-disciplinary projects across multiple stakeholders and/or facilities.
• Strong understanding of data standards, interoperability and governance challenges, and scientific data workflows.
• Ability to set priorities to accomplish multiple tasks within deadlines and adapt to changing needs.
• Knowledge of federal data policies, open science initiatives, and FAIR data principles.

Preferred Qualifications:

• Active Department of Energy Q clearance (SCI is a plus).
• Ph.D. in Computer Science, Information Science, or related field.
• Motivated self-starter with the ability to work independently and to participate creatively in collaborative teams across the laboratory.
• Experience working within or collaborating with national laboratories, federal research programs, or large-scale scientific facilities.
• 10+ years experience in designing systems that handle different modalities of scientific and national missions relevant data (structured and unstructured), storage (monolithic, federated, distributed, etc.), and modes of analysis (real time, batch, interactive, etc.)
• Familiarity with leadership-scale high-performance computing (HPC) environments, data-intensive science, and the applicability of AI techniques and technologies to scientific problems
• Experience supporting user facilities or…