Associate Director, Data Engineer

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About the role

This is an in-office role based in Barcelona, ES, with a requirement to work a minimum of three days per week on-site . Remote or travel flexibility is not available. Introduction to role Are you ready to engineer reusable data capabilities that accelerate discovery and put trustworthy AI into scientists’ hands? As Associate Director, Data Engineer, you will shape enterprise-grade solutions that make complex R&D workflows simpler, faster, and more reliable—advancing pre-clinical science by making data findable, accessible, interoperable, reusable, and trustworthy. Working in a small, agile team within the R&D Data Office, you will bridge scientific needs and robust engineering, helping to deliver data aligned to our enterprise semantics and standards. You will turn scientific and business requirements into modular, configurable capabilities that can be rapidly adapted across projects such as SafetyOmics and AI4Assay—managing multi-omics assay data from planning through to insight, and accelerating assay development with an AI co-scientist. How would you design solutions that adapt by configuration rather than redevelopment, so that each new use case benefits from what came before? Your work will enable automation, strengthen metadata integrity, and speed decision-making—impact that is visible and valued at every level of the organization. Accountabilities • Technical Solution Development : Prototype, design, develop, and deploy modular and configurable data solutions for complex end-to-end R&D workflows. Translate scientific and business requirements into clear designs and implementation plans. Prefer configuration over redevelopment to enable reuse across an expanding portfolio. Move solutions from prototype to production with operational readiness, lifecycle management, and continuous improvement. • Architecture and Operations Alignment: Work within AstraZeneca’s architecture, security requirements, and operational constraints, making pragmatic recommendations when options or limitations arise. Apply modern engineering practices including version control, automated testing, CI/CD, monitoring, observability, and documentation. • Technical Leadership and Delivery : Evaluate technical options and advise on trade-offs across scalability, maintainability, security, cost, performance, and reuse. Plan and track delivery, manage risks and dependencies, and provide timely, transparent updates. Collaborate with solution architects, data engineers, semantic modellers, and other specialists to deliver robust, supportable products. • Team Development and Coordination : Mentor junior colleagues and contribute to a culture of technical learning, constructive challenge, and continuous improvement. Coordinate delivery across internal contributors and outsourced partners. Troubleshoot issues affecting performance, data flows, metadata integrity, and production reliability. • FAIR+T and AI-Enabled Ways of Working : Engineer solutions that implement FAIR+T principles with end-to-end traceability, discoverability, and trustworthy use. Engage on data quality, metadata, and governance, resolving issues with the right partners. Identify where AI-assisted development, automation, and agent-based approaches can safely improve productivity and data-management workflows, assessing reliability, security, maintainability, and user needs. • Impact and Progression : In your first months, map critical workflows and deliver a high-value prototype for initiatives such as SafetyOmics or AI4Assay. Within 6–12 months, standardize and scale reusable capabilities that reduce assay cycle time and improve data traceability. Longer term, help shape enterprise data standards and platforms that enable automation and insight generation across pre-clinical research

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