Associate Director, Data Management

Introduction to role:

This role is based in
Barcelona
, with an on-site commitment of three days a week. Fluency in
English
is required.

Are you ready to build AI‑ready data products that power breakthrough insights for people living with rare and devastating diseases? Can you translate high‑value AI use cases into governed, interoperable datasets that scientists, clinicians, and business leaders trust to make faster, better decisions?

In this role, you will lead the creation and growth of AI‑ready data products and a data marketplace that directly enable responsible AI, advanced analytics, and real‑world evidence generation. You will connect enterprise standards to practical delivery, ensuring data is compliant, reliable, and engineered for models that matter. Your work will accelerate how we identify opportunities, reduce risk, and advance access—turning complex data into meaningful outcomes for under‑served patients and the teams supporting them.

• AI Ready Data Products Leadership: Lead the development and ongoing management of AI‑ready data products and a data marketplace that support strategic objectives and deliver sustained value.
• FAIR and Responsible AI Execution: Operationalize FAIR principles, model risk management, and responsible AI requirements across the data lifecycle.
• Regulatory and Governance Compliance: Ensure data products comply with GDPR, HIPAA, and internal governance standards from ingestion through consumption.
• AI‑Assisted Data Management: Deploy capabilities such as automated metadata extraction, schema discovery, and semantic tagging to increase speed and consistency.
• AI‑Driven Data Quality: Implement services that auto‑detect anomalies, drift, and outliers; trigger self‑healing rules; and propose fixes, with human‑in‑the‑loop approvals for high‑risk changes.
• Use Case Translation: Convert priority AI use cases into data requirements, delivery roadmaps, and acceptance criteria for AI‑ready datasets, feature stores, and knowledge graphs.
• Cross‑Functional
  
  Collaboration:
  
  Partner with enterprise governance, function‑specific data teams, IT business partners, capability leads, and business stakeholders to collect requirements and maximize value.
• Semantic Enablement: Champion ontologies, controlled vocabularies, and knowledge graphs to enable interoperable, context‑rich data consumable by LLMs and graph‑enabled analytics.
• Enterprise Data Strategy Support: Contribute to the broader strategy for AI‑ready data, data governance, and reference/master data management.
• Conceptual Modeling: Assure delivery of conceptual data models optimized for AI consumption, including feature schemas, ontology mappings, and metadata necessary for model reproducibility, aligned with the enterprise data model and future AI‑ready capabilities.
• Risk Management: Identify and mitigate risks related to data security, access, and continuity as products mature and usage patterns evolve.
• Performance and Insights: Monitor usage, performance, and KPIs of mature data products; provide clear reporting and actionable insights to leadership and stakeholders.
• Adoption and Enablement: Drive end‑user satisfaction through training, documentation, and support; communicate updates, changes, and value propositions effectively.
• Stakeholder Engagement: Present plans, progress, and outcomes to senior management and key stakeholders with clarity and confidence.
• Team Leadership: Mentor data management professionals; foster a culture of innovation, continuous improvement, and professional growth.

• Bachelor’s or Master’s degree in Data Science, Information Management, Computer Science, or related field.
• Extensive experience (typically 7+ years) in Data Management, Data Products, Data Quality, and Metadata Management, with at least 3+ years in a leadership role.
• Hands‑on experience with Data Ops platforms such as Collibra and cloud‑based data integration technologies.
• Strong leadership, problem‑solving, and communication skills, with the ability to translate technical concepts for non‑technical audiences.
• Awareness of principles related to building AI‑ready data foundations.
• Experience in operationalizing…