Data Architect - Scientific Compute job opportunity at Ellison Institute of Technology.

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Date2026-02-23T19:56:18.572Z bot
Ellison Institute of Technology Data Architect - Scientific Compute
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loacation Oxford, United Kingdom
loacation Oxford....United Kingdom

Join us at EIT: 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:   Health, Medical Science & Generative Biology Food Security & Sustainable Agriculture Climate Change & Managing CO₂ Artificial Intelligence & Robotics   This is ambitious work - work that demands curiosity, courage, and a relentless drive to make a difference. At EIT, you’ll join a community built on excellence, innovation, tenacity, trust, and collaboration, where bold ideas become real-world breakthroughs. Together, we push boundaries, embrace complexity, and create solutions to scale ideas from lab to society. Explore more at www.eit.org .   Your Role: 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.   Your Responsibilities: Data & Storage Architecture Collaboration 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. Standards, Schemas & Consistency 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. Large-Scale Data Integration & AI Enablement 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. Data Governance, Classification & Compliance 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. Collaboration & Influence Act as a trusted partner to scientific compute leaders across programmes, engaging deeply with their requirements, constraints, and research priorities. Lead through influence rather than mandate, collectively defining shared schemas and standards that programmes commit to and adopt. Support programmes with the operationalisation of agreed standards, ensuring they are embedded into delivery pipelines and day-to-day practices. Operationalisation & Enablement Ensure data architecture standards move beyond definition into implementation and sustained operation. Provide guidance, reference architectures, and hands-on support to programme teams adopting shared data and storage standards. Work alongside platform, DevOps, and operations teams to embed standards into tooling, automation, and operational processes.  Essential Skills, Qualifications & Experience: Extensive, demonstrable experience as a Data Architect operating at enterprise or institute-wide scale. Proven experience designing data and storage architectures for large-scale scientific, research, or compute-intensive environments. Strong experience defining and governing data standards, schemas, metadata models, and configuration baselines across multiple teams. Deep understanding of data governance, classification, privacy, and compliance in complex organisations. Demonstrated ability to integrate data across domains to support advanced analytics and AI/ML use cases. Significant experience influencing senior technical, scientific, and data stakeholders in matrixed organisations.   Desirable Knowledge, Skills and Experience: Background in scientific research, HPC, AI/ML platforms, or data-intensive research environments. Experience supporting large-scale AI or LLM training pipelines. Experience modernising or standardising data architectures across federated or decentralised research teams. Experience operating within large-scale cloud-based data platforms.

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