Sr Director, Enterprise Data & AI Architecture, Orthopedics

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and Med Tech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn

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As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

Job Function:

Enterprise Management

Job Sub Function:

Technology Management

Job Category:

People Leader

All Job Posting Locations:

Raritan, New Jersey, United States of America

Job Description:

Role Overview

This is a critical executive leadership role responsible for defining, governing, and evolving the enterprise-wide Data, Analytics, AI, and Digital Architecture strategy for a newly forming standalone company. Reporting directly to the Chief Data & Analytics Officer (CDAO), the Chief Data & AI Architect serves as the senior-most technology and architecture authority for all data and AI platforms, products, and capabilities across the enterprise.

The role requires a highly experienced technology leader with deep expertise in modern data architecture, cloud platforms, AI/ML engineering, Generative AI, Agentic AI, enterprise integration, and large-scale data transformation programs. This individual will establish the target-state architecture, technology standards, operating model, and investment roadmap to build a scalable, secure, and intelligent enterprise platform that accelerates business growth and innovation.

The Chief Data & AI Architect will partner closely with executive leadership, business functions, enterprise technology teams, and global capability centers (GCCs) to ensure all data and AI investments align with enterprise strategy while enabling new AI-driven ways of working.

This is a unique opportunity to architect and build a modern Data & AI ecosystem from the ground up, leveraging cloud-native technologies, intelligent automation, agentic workflows, advanced analytics, and emerging AI capabilities that will define the future operating model of the organization.

Key Responsibilities

Enterprise Data & AI Architecture Leadership

* Define and own the enterprise-wide Data, AI, Analytics, and Information Architecture strategy, standards, principles, reference architectures, and multi-year roadmap.

* Serve as the organization's principal architect and trusted advisor to the CDAO, executive leadership, and technology teams on all data and AI-related investments and decisions.

* Establish the target-state architecture for enterprise data platforms, AI ecosystems, digital products, knowledge systems, and intelligent automation capabilities.

Data Platform & Modern Data Architecture

* Design and oversee modern cloud-native data architectures including Data Warehouses, Data Lakes, Lakehouse platforms, Data Mesh, Data Fabric, Metadata Management, Master Data Management (MDM), and Data Products.

* Lead architectural decisions for enterprise-scale data ingestion, integration, streaming, event-driven architectures, API ecosystems, and real-time analytics platforms.

* Define scalable patterns for data interoperability across ERP, CRM, Manufacturing, Medical, Commercial, and Enterprise systems.

AI, Generative AI & Agentic Architecture

* Architect enterprise AI platforms supporting Machine Learning, Predictive Analytics, Generative AI, Retrieval-Augmented Generation (RAG), Knowledge Graphs, Vector Databases, and Multi-Agent Systems.

* Define the enterprise strategy for Agentic AI capabilities, autonomous workflows, AI assistants, digital workers, intelligent orchestration, and human-in-the-loop operating models.

* Establish architecture patterns for AI governance, model lifecycle management, prompt engineering,…