Associate Director, Data Engineer

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. Why this role matters AstraZeneca is transforming research and development through better use of data, technology, and artificial intelligence. Within the Enterprise AI Unit, Enterprise Data Enablement develops the data capabilities needed to support AstraZeneca’s core business. The R&D Data Office is building enterprise-level capabilities to make R&D data FAIR+T— findable, accessible, interoperable, reusable, and trustworthy . Reliable data and metadata pipelines are essential to achieving this ambition. They enable data to move efficiently from scientific and laboratory systems into trusted, discoverable, and reusable data products that can support analysis, automation, and AI-enabled discovery. As an Associate Director, you will design, implement, deploy, and maintain Data pipelines that support the end-to-end management of data and metadata across R&D priority programmes . You will work on initiatives such as SafetyOmics and AI4Assay, helping ensure that internally generated data, including multi-omics data, is captured, processed, enriched, validated, traced, and made available for scientific use. This is a hands-on individual contributor role. You will work closely with team leadership and technical colleagues to turn project needs into robust, production-ready pipeline solutions. Although the initial focus will be on specific projects, the pipelines you will develop should be modular, configurable, and reusable across a growing range of R&D use cases. What You Will Do Data Pipeline Engineering • Design, build, test, deploy, and maintain robust pipelines for the ingestion, processing, transformation, validation, enrichment, and publication of R&D data and metadata. • Deliver solutions supporting the end-to-end data lifecycle, from source-system integration through to FAIR data publication, scientific analysis, and downstream use. • Integrate data from internal systems, laboratory applications and instruments, APIs, databases, and file-based sources. • Build modular, configurable, and reusable pipelines that can support multiple projects and evolving scientific requirements. • Implement appropriate data quality, validation, provenance, traceability, and error-handling controls. • Develop supporting APIs, services, workflow components, and data infrastructure where required. Production Delivery & Engineering Excellence • Take solutions from concept and prototype through production deployment, monitoring, troubleshooting, maintenance, and continuous improvement. • Apply strong software engineering practices, including version control, automated testing, CI/CD, observability, security, documentation, and controlled change management. • Optimize pipelines for reliability, scalability, performance, maintainability, and cost efficiency. • Contribute hands-on code and help shape technical decisions and engineering approaches. Collaboration and Innovation • Partner with scientists, bioinformaticians, data engineers, architects, data-management specialists, and other stakeholders to translate scientific and business requirements into practical technical solutions. • Support appropriate metadata structures, FAIR+T principles, data quality, and data-management standards. • Clearly communicate technical options, constraints, and solutions to both technical and non-technical audiences. • Coordinate delivery with colleagues and outsourced service provider contractors where required. • Identify opportunities to use automation and AI-enabled approaches to improve data-management and engineering workflows. • Contribute to technical learning, knowledge sh