Data Architect - Scientific Compute Business Operations & England, United

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At the Ellison Institute of Technology (EIT), we’re on a mission to translate scientific discovery into real world impact. We bring together visionary scientists, technologists, engineers, researchers, educators and innovators to tackle humanity’s greatest challenges in four transformative areas:

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At EIT we are seeking a Data Architect with demonstrable experience to enable the definition, ownership, and evolution of the data and storage architecture underpinning scientific compute across the institute. This role is central to enabling large‑scale, cross‑disciplinary science by ensuring data can be consistently integrated, governed, and exploited across programmes, including the creation of very large datasets used to train advanced AI and LLM models addressing some of the world’s most enduring challenges.

Success in this role depends as much on collaboration and influence as on technical excellence. While the role owns institute‑wide data architecture standards, schemas, and configurations, these will be maintained in close partnership with scientific compute leaders across programmes, collectively defining shared approaches and supporting their operationalisation. This is a hands‑on leadership role for a seasoned data architect with demonstrable experience operating at scale in complex, compute‑intensive scientific or research environments.

• Collaborate with research group technical leadership to define and continuously evolve the institute‑wide data and storage architecture supporting large‑scale scientific compute.
• Define target‑state data architectures for scientific data that enable EIT’s institutes to do their best work, balancing standardisation, flexibility, scalability, performance, resilience, and security across heterogeneous scientific data workloads.
• Work hand‑in‑glove with other data teams within the Institute including Data Engineering (within AI and Robotics) and Enterprise Applications to ensure strategic and operational alignment across disciplines.
• Translate organisational strategy and scientific priorities into coherent data architecture roadmaps for scientific compute.

• Define and own institute‑wide data standards that are utilised by our scientists and developers, including schemas, metadata models, naming conventions, and configuration baselines.
• Ensure that consistent standards, schemas, and configuration settings are used across all scientific programmes, wherever appropriate.
• Balance standardisation with scientific flexibility, providing clear, governed patterns for extension rather than divergence.

• Architect approaches for integrating data across scientific programmes into unified, high‑quality datasets.
• Enable the creation of very large datasets suitable for advanced analytics, machine learning, and large language model training.
• Work closely with scientific compute, AI, and platform teams to ensure data architectures are optimised for large‑scale downstream consumption.

• Be accountable for aligning data and storage operational standards with data classification models defined by the Institute’s Data Protection Officer (DPO) and other data specialists.
• Translate governance, privacy, and security requirements into clear, practical architectural and operational standards.
• Ensure data is handled appropriately throughout its lifecycle, including ingestion, storage, access, sharing, retention, and deletion.

• Act as a trusted…