Head of Data Platforms

The employer is a global biotechnology company focused on developing innovative therapies for serious diseases, combining scientific research with advanced technology to deliver impactful treatments and improve patient outcomes worldwide. The role is within the Technology & Telecoms industry in the IT Data Analysis area.

• 10+ years of experience in data platforms, data engineering, cloud data architecture, or a related domain.
• 5+ years of experience leading managers, senior engineers, architects, and global technical teams.
• Deep expertise in modern data platform architectures, including lakehouse and cloud-native data ecosystems.
• Hands-on experience with enterprise-scale cloud platforms, particularly Azure and/or AWS.
• Strong knowledge of Databricks or comparable large-scale analytics platforms.
• Experience delivering AI and GenAI platform capabilities, including MLOps, LLMOps, retrieval-augmented generation (RAG), vector search, model serving, and AI-powered analytics.
• Strong background in Infrastructure as Code, automation, DevOps, and platform engineering.
• Proven success driving cloud FinOps, governance, cost optimization, and operational excellence initiatives.
• Familiarity with data governance, master data management, integration platforms, and enterprise data management practices.
• Experience operating within complex, highly regulated environments is highly desirable.
• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related discipline; advanced degree preferred.

Platform Strategy & Architecture:

• Define and execute a multi-year vision and roadmap for enterprise data and AI platforms.
• Establish standards, frameworks, and best practices for data ingestion, integration, streaming, semantic models, analytics, and AI workloads.
• Design and evolve scalable platform architectures supporting multiple business domains and geographic regions.
• Lead architecture governance, technology assessments, and strategic platform investment decisions.
• Drive platform simplification initiatives by reducing tool fragmentation and technical debt.
• Manage strategic technology partnerships and vendor relationships.

AI & Advanced Analytics Enablement:

• Leverage AI to improve metadata management, data quality, lineage, documentation, operational efficiency, and platform support.
• Define enterprise patterns for AI-enabled analytics, generative AI, conversational BI, retrieval-augmented generation (RAG), model observability, and responsible AI practices.
• Partner with business and technology stakeholders to scale successful AI initiatives into enterprise-wide capabilities.
• Maintain an innovation roadmap balancing business value, operational excellence, risk management, and regulatory requirements.

FinOps & Cost Optimization:

• Own financial governance and cost efficiency across the data and AI platform landscape.
• Lead capacity planning, budget management, and cloud cost optimization initiatives.
• Establish cost transparency through chargeback/showback models, workload governance, and usage monitoring.
• Define measurable targets and KPIs for platform efficiency and value realization.

Engineering, Operations & Reliability:

• Oversee platform engineering, cloud infrastructure, access management, automation, CI/CD, monitoring, and resilience.
• Deliver platform-as-a-product capabilities, including self-service provisioning, reusable frameworks, deployment templates, and developer enablement.
• Introduce and mature Site Reliability Engineering (SRE) practices covering availability, performance, incident response, capacity planning, and disaster recovery.
• Govern reusable accelerators, frameworks, and platform services as managed products with defined roadmaps and release plans.
• Ensure adherence to enterprise security, privacy, compliance, audit, and governance standards.
• Implement end-to-end observability for data products, pipelines, workloads, and operational health.

Leadership & Operating Model:

• Build and lead a global team responsible for enterprise data and AI platform capabilities.
• Define effective engagement models between centralized platform organizations and distributed domain teams.
• Collaborate with senior business and technology leaders to align platform investments with strategic priorities.
• Foster communities of practice and drive adoption of platform standards and best practices.
• Develop technical talent through coaching, mentorship, architecture reviews, and capability-building initiatives.