Team Lead Engineering Terminology Management Platform

At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections, where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters. The Position Team Lead Engineering Terminology Management Platform Job description : As a senior global engineering leader, you will sit at the intersection of AI innovation and enterprise scale, building and leading high-performing engineering teams that are defining the future of AI-ready data infrastructure. You will empower your teams to own their career journeys while fostering an ambitious culture of technical excellence, psychological safety, and rapid experimentation. By pairing servant leadership with strong executive presence, you will remove organizational silos, align complex stakeholder networks, and build a world-class engineering capability capable of delivering next-generation semantic and contextual AI platforms at a global scale. Description of the area: The Data Platforms function is paving the way for a context-aware enterprise where AI doesn't just read data-it understands it. We are building the backbone for AI for Data and Data for AI, unlocking domain context across the entire value chain. This role oversees 3 critical, high-impact enterprise platforms: Terminology Service: Enabling scientific breakthroughs by unifying terminology, domain ontologies, and multi-modal knowledge graph layers for target discovery and early innovation. Enterprise Core Semantic Layer: Orchestrating unified business logic, metadata, and enterprise knowledge graphs to power context-aware LLMs, AI agents, and trusted self-service analytics across global operations. Globally Unique Identifiers: Connecting complex operational systems into an intelligent knowledge network to optimize supply chain resilience, commercial execution, and predictive financial modeling. By replacing fragmented data silos with intelligent context layers, semantic platforms, and dynamic knowledge graphs, this leader will establish the foundation for enterprise-wide autonomous agentic workflows and true AI readiness. Job responsibilities: Team Member Capability and Career Development Proactively identifies and develops future-focused engineering team capabilities, aligning with long-term strategic goals. Mentors team members on long-term career paths, creating new opportunities and roles within the organization to support their growth. Addresses and helps resolve complex, systemic roadblocks that impact multiple development teams, and coaches other managers on effective performance management. Oversees the technical and soft skill development, ensuring a balanced and high-performing talent portfolio. Team Health and Sustainability Establishes and monitors metrics for team health and sustainability across several development teams, implementing strategies to ensure long-term balance. Designs and implements initiatives to improve collaboration and psychological safety across multiple related development teams or a whole product area. Proactively identifies and resolves systemic impediments that affect multiple development teams, improving the overall work environment. Manages the talent composition for own and related teams, planning for future needs and ensuring a healthy pipeline of skills. Product and Service Delivery Leadership Co-creates the multi-year architecture vision and technology strategy for our 3 core contextual platforms, partnering closely with AI, R&D, Clinical, Commercial, DIA, Manufacturing, Group Processes, and Enterprise Product Leaders. Architect of "Data for AI & AI for Data": orchestrates dependencies across complex platform pipelines to ensure real-time enterprise context is discoverable, actionable, and secure. Drives initiatives to improve engineering processes and collaboration models across a whole product group or department. Accountable for the global delivery, reliability, and security of enterprise knowledge graphs, semantic layers, and ontology management services powering generative AI and LLM agents. Navigates high-stakes, cross-functional dependencies across global business units, aligning senior stakeholders on unified semantic architecture standards. Drives continuous modernization of platform engineering, CI/CD for ontologies/graphs, and lean agile methodologies across the global data and AI engineering organization. Performance & Optimization Defines success metrics around AI context quality, semantic query latency, ontology adoption, and measurable business impact across scientific and operational domains. Synthesizes complex technical insights into compelling strategic narratives for leadership, directly influencing future AI and enterprise data investments. Spearheads step-change optimization efforts in enterprise data mesh architectures, ontology evolution, and semantic graph performance to deliver rapid scale. Financial & Resource Mgmt. Supports the development of business cases for large initiatives or new product lines, working with product and finance partners to justify investment. Manages resource allocation across multiple projects, making trade-offs to optimize for portfolio-level goals and strategic priorities. Manages the team's budget, including headcount, operational costs, and vendor contracts, ensuring financial responsibility. Qualifications: Experience: 10+ years of progressive engineering and people leadership experience in global tech, enterprise software, big tech, consulting or high-scale regulated healthcare/life sciences organizations. Proven track record of leading distributed, multi-disciplinary engineering teams that build scale-out platform products, data platforms, or AI/ML pipelines. Demonstrated experience driving enterprise-scale data transformations, semantic layers, or knowledge graph platforms. Experience navigating regulated environments (e.g., healthcare, biotech, pharmaceutical, or finance) is highly desirable. Education Advanced degree (Master’s or Ph.D.) or equivalent experience in Computer Science, Computational Biology, Artificial Intelligence, Information Systems, or a related quantitative field (nice-to-have) Technical & business s