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 sharing, and continuous improvement across the team.
What We Are Looking For
Required Qualifications
- Strong experience designing, developing, deploying, and maintaining complex data pipelines or data-intensive software solutions.
- Strong hands-on Python programming skills and familiarity with other relevant programming languages.
- Practical experience with AWS services such as S3, Lambda, Glue, Step Functions, RDS, CloudWatch, and IAM, or equivalent cloud technologies.
- Experience with relational databases, APIs, data integration, workflow orchestration, and data-processing frameworks.
- Proven experience taking pipelines from prototype into production, including deployment, monitoring, troubleshooting, lifecycle management, and continuous improvement.
- Strong understanding of data quality, validation, error handling, provenance, and traceability.
- Ability to design modular, reusable, configurable, and maintainable technical solutions.
- Experience applying software engineering practices including Git/version control, automated testing, CI/CD, observability, documentation, and secure development.
- Practical knowledge of automation and AI-assisted development for pipeline development, testing, documentation, monitoring, and maintenance.
- Hands-on experience with coding agents, such as Claude Code or Codex, for pipeline prototyping, development, or testing.
Preferred Qualifications
- Understanding of AI agents and agent orchestration, including agent frameworks, guardrails, Model Context Protocol (MCP), agent skills, and integration with data sources and pipelines.
- Understanding of security, governance, privacy, and responsible use in relation to AI agents and MCP-based tools.
- Experience with bioinformatics, multi-omics, genomics, laboratory data, or scientific data workflows.
- Experience within discovery, pre-clinical, translational, clinical, or other life-sciences research environments.
- Experience with non-relational databases, containers, Kubernetes, infrastructure-as-code, or hybrid cloud environments.
- Experience working with large datasets, scientific computing, or high-performance data processing.
- Exposure to statistical, machine-learning, or AI techniques within data or scientific workflows.
- Experience working in regulated or quality-controlled environments.
- Experience mentoring colleagues or coordinating technical delivery through influence.
#EAI
Date Posted
05-nov-2026
Closing Date
03-dic-2026
AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.
