Associate Director, Data Engineer
AstraZeneca · Barcelona, Spain · Global
Clinical Data Science
Remote
Not yet analysed
Demand84
Career growth82
Healthcare fit52
Remote potential55
5-year outlook
Likely aligned with senior/lead-level pharma data engineering compensation, with upside as AI-enabled and FAIR data platforms become more central to R&D. Exact salary is not stated and should be verified directly.
Clinical license
Not required
Applicant reach
narrow
Seniority
lead
Employment type
full-time
Date found
2026-11-05
Intelligence details
Flexible work details
- Engagement type
- Permanent
- Hours
- Full-time
Eligibility, licensing, and location restrictions can change. Always confirm details on the source posting before applying.
Why this is worth applying to
Worth considering only if you have strong hands-on data engineering, cloud/CI-CD, and life-sciences R&D data experience. Clinicians without a serious technical data-engineering base are unlikely to be competitive.
Who should apply
Clinician-informaticians, physician-scientists or pharmacists with data engineering/platform experience, healthcare graduates with strong software/data skills, and technical leaders with pharma R&D data experience.
CV angle
Frame clinical/healthcare experience as domain expertise in R&D data workflows, data quality and governance, metadata, security awareness, and translating scientific requirements into technical solutions. Highlight concrete data pipelines, code, CI/CD, automated testing, cloud/data-platform work, and any multi-omics or assay-data projects; do not overstate engineering experience.
Skills to highlight
- Data engineering
- FAIR data principles
- Metadata and data governance
- Multi-omics and assay data workflows
- CI/CD and automated testing
- Cloud/data platform architecture
- Stakeholder translation
- Technical leadership
Role summary
Senior data engineering role in AstraZeneca's R&D Data Office, building modular, configurable, production-grade data solutions and standards for pre-clinical research, multi-omics assay data, metadata integrity, and AI-enabled workflows.
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