Overview

In this role you will engineer reusable, enterprise-grade data capabilities to accelerate discovery and enable trustworthy AI in pre-clinical science. You will operate within the R&D; Data Office, turning scientific needs into modular, configurable data solutions and guiding them from prototype to production. You will collaborate with cross-functional teams to improve data traceability, metadata integrity, and decision speed, while exploring AI-assisted approaches to boost productivity. This is a hands-on leadership role focused on scalable, standards-aligned data platforms with tangible impact across projects like Safety Omics and AI4Assay.

Responsabilidades

- Prototype, design, and deploy modular data solutions for complex R&D; workflows
- Translate scientific and business needs into clear designs and implementation plans
- Favor configuration over redevelopment to maximize reuse
- Move solutions from prototype to production with lifecycle management and continuous improvement
- Align with Astra Zeneca architecture, security, and operational constraints
- Apply modern engineering practices: version control, automated testing, CI/CD, monitoring, documentation
- Provide technical leadership, evaluate options, and manage trade-offs across scalability, security, cost, and reuse
- Mentor junior colleagues and coordinate delivery across internal and external partners
- Contribute to FAIR+T data practices,



data quality, metadata governance, and AI-enabled workflows
- Map critical workflows and deliver high-value prototypes within first months, then standardize and scale reuse

Requisitos principales

- Designing, developing, deploying, and maintaining data and software solutions for complex workflows
- Strong Python programming and familiarity with other relevant languages
- Experience with AWS or equivalent cloud services (S3, Lambda, Step Functions, Cloud Watch, IAM)
- Relational databases and data integration/processing workflows
- Ability to design modular, configurable, maintainable, reusable solutions
- Translate business/scientific needs into technical requirements and pragmatic solutions
- Experience delivering in agile environments with risk/dependency management
- Ability to evaluate technical options and explain trade-offs related to scalability, maintainability, security, cost, performance, reuse
- Understanding of data quality, metadata, FAIR principles, and troubleshooting data issues
- Interest in AI capabilities, automation, and agent-based solutions to improve workflows

- Collaboration and cross-functional communication
- Mentoring and technical leadership
- Problem-solving and adaptability
- Python programming
- AWS cloud services (S3, Lambda, Step Functions, Cloud Watch, IAM)
- Relational databases

📌 Associate Director, Data Engineer (Barcelona)
🏢 AstraZeneca
📍 Barcelona