Data engineer in life sciences

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.

• 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.
• 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.
• 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.

• 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.
• Будет плюсом: 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

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