Data engineer for pre-clinical science

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.

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

• Demonstrated experience designing, developing, deploying, and maintaining data and software solutions that support complex business, scientific, or operational workflows.
• Strong programming experience in Python and working knowledge of other relevant programming languages.
• Practical experience developing and deploying solutions using AWS or other cloud services, with familiarity with services such as Amazon S3, AWS Lambda, AWS Step Functions, Amazon CloudWatch, AWS IAM or equivalent.
• Experience working with relational databases and data integration or processing workflows.
• Ability to design modular, configurable, maintainable, and reusable solutions.
• Ability to translate business or scientific needs into technical requirements and pragmatic solution approaches.
• Experience planning and delivering technical work in an agile environment, including tracking progress, managing risks and dependencies, and reporting to stakeholders.
• Ability to evaluate technical options and clearly explain trade-offs relating to scalability, maintainability, security, cost, performance, and reuse.
• Understanding of, or strong interest in, data quality, metadata, and FAIR principles, with the ability to contribute to troubleshooting issues affecting data and metadata.
• Interest in using AI capabilities, including AI-assisted development, automation, and agent-based solutions, to improve engineering productivity and data-management workflows.
• Будет плюсом: Experience with non-relational databases, such as graph, document, key-value, or analytical databases; Experience with data integration, metadata management, semantic web technologies (RDF, OWL, SHACL, SPARQL), data governance, or FAIR data practices; Interest in, or experience of, pharmaceutical R&D, particularly pre-clinical research, laboratory workflows, or scientific data; Experience working in regulated, privacy-sensitive, or otherwise controlled environments; Experience mentoring colleagues or coordinating technical delivery through influence; Experience applying AI agents or automation to scientific, data-management, or software-development workflows

• You will collaborate with deep specialists and diverse partners, often bringing unexpected teams into the same room to unlock bold ideas.
• With strong backing from leadership, your engineering will power faster, higher-quality decisions and reliable operations across the enterprise, directly influencing how quickly lifesaving science reaches patients.
• You will have access to modern platforms, the latest methods in data and machine learning, and supportive communities that value curiosity, constructive challenge, and kindness alongside ambition.
• We place data at the heart of how we operate and encourage thoughtful, ethical use of AI and automation—balancing pace, rigor, and traceability so your work stands the test of time.