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

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

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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

enterprise architecturedata architecturedata integrationAPI-firstevent-drivenstreamingbatch processingdata governancedata modelinganalytics ecosystems

Soft Skills

leadershipcommunicationinfluencepragmatic executioncollaborationstrategic thinkingproblem-solvingoperational efficiencydata stewardshipcross-functional teamwork