Enterprise Architect - Life Sciences

Roles and Responsibility

• Define and maintain enterprise architecture vision, principles, standards, and roadmaps aligned with life sciences business strategy.
• Develop current-state and target-state architectures across business, application, data, and technology domains.
• Drive architecture for digital transformation initiatives such as data platforms, AI/ML enablement, cloud migration, and legacy modernization.
• Align IT architecture with R&D, Clinical, Manufacturing, Quality, Regulatory, and Commercial strategies.
• Provide architectural leadership across Research & Discovery, ensuring architecture supports end-to-end data flow across the life sciences value chain.
• Ensure architecture complies with GxP, 21 CFR Part 11, GDPR, HIPAA, and data privacy regulations, embedding security, auditability, traceability, and validation requirements into design.
• Partner with business leaders, scientific teams, and IT leadership to translate strategy into executable architecture, acting as a trusted advisor on technology choices and trade-offs.
• Support large-scale programs and portfolios with architecture oversight, mentoring Solution Architects and technical teams on best practices.
• Promote reuse of standards, platforms, and reference architectures, contributing to improving enterprise architecture maturity.

Job Requirements

• Strong experience with Enterprise Architecture frameworks (TOGAF or equivalent).
• Hands-on experience in Application Architecture (Monoliths, Microservices), Data Architecture (Data Warehousing, Data Lakes, MDM), Integration Architecture (APIs, ESB, Event-driven), and Cloud Architecture (AWS / Azure / GCP).
• Experience modernizing legacy life sciences systems and familiarity with regulated environments (GxP).
• Strong stakeholder management and communication skills, with the ability to influence across business, scientific, and IT teams.
• Strategic mindset with strong problem-solving ability and comfort working with senior leadership.
• Ability to work in a fast-paced environment and adapt to changing priorities.
• Experience with tools such as Sparx EA, LeanIX, ArchiMate, or equivalents.
• Familiarity with clinical, scientific, and manufacturing data landscapes.
• Tools & Platforms: AWS / Azure / GCP, Data & analytics platforms: Data Lakes, Warehouses, Analytics tools, Integration platforms: APIs, Messaging, Middleware.
• Soft Skills: Strong stakeholder management and communication skills, ability to influence across business, scientific, and IT teams, strategic mindset with strong problem-solving ability, comfortable working with senior leadership.