About The Position
The Life Sciences Industry Enterprise Architect (Business Technology Architect – Data & Analytics) is a strategic technology leader responsible for defining, designing, and governing enterprise‑scale data and analytics architecture that enables regulated, data‑driven Life Sciences operations. This role partners closely with R&D, Clinical, Regulatory, Pharmacovigilance/Safety, Manufacturing, Supply Chain, Commercial, Finance, Quality, Security, and Data & AI teams to ensure the enterprise data ecosystem supports compliance, integrity, scalability, insight generation, and AI enablement. Enterprise Architects (Business Technology Architects) define and structure the enterprise’s business and technology strategy, capabilities, services, governance, organizational domains, and key business processes, translating business intent into end‑to-end architecture outcomes. In this role, the focus is on data as a core enterprise asset—connecting transactional platforms, analytical systems, and AI‑enabled capabilities across the Life Sciences value chain. The architect aligns goals and objectives with platforms (package‑based and custom), data products, integration services, and analytics capabilities to optimize enterprise decision‑making and operational performance. This role requires deep understanding of Life Sciences data dimensions and semantics, including clinical, manufacturing, quality, supply, commercial, and finance data, across Pharmaceuticals (Innovator & Generics), Biotechnology, MedTech & Medical Devices, Diagnostics, Consumer Health, and the extended ecosystem of CROs, CMOs/CDMOs, distributors, and channel partners. The architect must balance data governance, regulatory expectations, and AI readiness while enabling speed, reuse, and scale.
Requirements
- Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field (or equivalent experience).
- 12+ years of experience in enterprise architecture, data architecture, or technology leadership, ideally within Life Sciences or other regulated, data‑intensive environments.
- Demonstrated experience architecting enterprise data platforms, analytics ecosystems, and AI‑ready data foundations at scale.
- Strong understanding of Life Sciences data domains, data semantics, and cross‑functional data dependencies.
- Deep knowledge of cloud platforms (Azure/AWS/GCP), data platforms, integration architectures, APIs, event‑driven patterns, and modern analytics architectures.
- Experience establishing enterprise data architecture governance, standards, and roadmaps; ability to influence adoption across engineering, data, product, and business teams.
- Hands‑on experience using AI‑enabled tools (e.g., Copilot‑class assistants, AI‑assisted data modeling and analysis) to accelerate architecture definition and delivery.
Responsibilities
- Managing Enterprise Architect (Business Technology Architect) – Design, deliver, and manage complete enterprise data and analytics architecture solutions, including capability‑based roadmaps, target‑state data architectures, reference architectures, and governance. Demonstrate leadership across the architecture and data community with a strong focus on business outcomes and pragmatic execution.
- Translate business outcomes (regulatory compliance, data integrity, insight timeliness, operational efficiency, AI enablement, revenue protection) into data architecture, reliability, quality, lineage, and observability requirements.
- Operate as a stream lead at CIO/CDO/CTO level for internal or external clients; lead Capgemini architecture execution related to data, analytics, and AI platforms, and contribute to market development and service delivery excellence.
- Own enterprise data architecture strategy and roadmap for Life Sciences, aligned to business priorities, growth plans, and operational realities (regulated delivery, validation readiness, global data distribution).
- Define and enforce data architecture standards and governance, including data domains, canonical models, data products, quality rules, lineage, and stewardship operating models. Proven ability to apply an architectural framework at enterprise scale.
- Architect and guide data platforms and integrations across package‑based Life Sciences systems and custom solutions, including ERP, LIMS, MES, QMS, CTMS, safety, regulatory content systems, manufacturing, supply, and commercial platforms.
- Define data integration and ingestion patterns, including API‑first, event‑driven, streaming, batch, and hybrid approaches, supporting both operational and analytical workloads.
- Establish enterprise data models and domain‑oriented data products, ensuring consistent definitions of core entities (product, patient, trial, batch, site, customer, supplier) across systems.
- Ensure data governance, privacy, and security are embedded by design, including lineage, auditability, access controls, and compliance with regulated Life Sciences expectations.
- Establish data reliability and observability principles (freshness, completeness, accuracy, latency, resiliency) aligned to Life Sciences operational and regulatory realities.
- Evaluate emerging data and AI technologies (GenAI, advanced analytics, intelligent document processing, knowledge graphs) for fit, feasibility, risk, and operational value in Life Sciences environments.
Benefits
- Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade, Company paid holidays, Personal Days, Sick Leave
- Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)
- Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)
- Life and disability insurance
- Employee assistance programs
- Other benefits as provided by local policy and eligibility
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
