Data & Analytics Director

About the role

A leading global pharmaceutical company is looking for a hands‑on Data & Analytics Director to own the strategy, architecture, delivery and operational performance of its enterprise data platform. The role combines senior leadership with deep technical involvement, ensuring the platform is AI‑ready, governed and model‑grade.

Key responsibilities

• Own and evolve the Azure‑based enterprise data platform (Azure Data Lake Storage Gen2, Databricks lakehouse, Power BI, Unity Catalog), setting engineering standards, data quality gates and CI/CD pipelines.
• Drive the platform roadmap, remediate technical debt, and prepare the platform for downstream AI and analytics demand.
• Lead the delivery programme from source ingestion through to data products, defining SLAs, sprint cadence, testing and release management.
• Design and operate data governance, lineage, business glossary and quality frameworks to meet regulated AI and GxP requirements.
• Build, hire and develop a multidisciplinary D&A team, establishing performance culture, career frameworks and engineering best practices.
• Partner with business leaders and vendors, translating data needs into platform priorities and communicating status, risks and trade‑offs.

Required profile

• Minimum 10 years of progressive experience in data engineering or enterprise analytics, with at least 5 years in senior leadership managing multidisciplinary technical teams.
• Proven hands‑on experience building or remediating cloud‑native data platforms in complex, multi‑source environments, preferably in life‑sciences or regulated industries.
• Demonstrated ability to lead data governance or catalogue implementations and to deliver AI‑ready data products.

Required skills

• Azure Data Lake Storage Gen2
• Databricks (Delta Lake, Unity Catalog)
• Power BI
• Medallion / lakehouse architecture
• CI/CD pipelines and data quality frameworks
• Data governance, lineage and business glossary tools
• Experience with AI/ML data readiness (feature stores, training datasets)