Principal Data Engineer

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 MedTech, 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 more at jnj.com
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:
Data Analytics & Computational Sciences
Job Sub Function:
Data Engineering
Job Category:
Scientific/Technology
All Job Posting Locations:
New Brunswick, New Jersey, United States of America
Job Description:
This is a duration based role that will last 2 years. Role can be fully remote.
The Principal Data Engineer- AI owns product engineering and architectural decisions, serving as both the technical visionary and hands-on leader responsible for solution delivery. This role partners closely with Product Owners, Product Group Engineers, Lead Engineers, architects, and cross-functional product squads to solve complex engineering challenges, define scalable technical solutions, and ensure alignment with enterprise technology strategy.
The role is accountable for product technical architecture, engineering standards, technology roadmaps, and the successful delivery of scalable, secure, and governed data and AI solutions. The ideal candidate brings 10+ years of progressive experience in enterprise data engineering, architecture, analytics, and AI, with deep expertise in Azure, Microsoft Fabric, Databricks, Power BI, Data Mesh, Data Federation, Data Modeling, Data Governance, Enterprise Data Management, Generative AI, and Agentic AI platforms.
Responsibilities / Key Jobs to be Done (Workday “What You Will Do”)
Define and own the overall engineering strategy, technology architecture, roadmap, and technical direction for the product in partnership with Product Owners, Product Group Engineers, and Business Unit Architects.
Ensure technical implementations align with enterprise architecture standards, business objectives, economic frameworks, and long-term technology strategy.
Partner with business stakeholders to shape product vision, define capabilities, and translate business requirements into scalable data, analytics, and AI solutions.
Provide hands-on technical leadership, actively contributing to architecture reviews, design reviews, code reviews, proof-of-concepts, and technical enablers.
Evaluate solution alternatives, validate concepts with stakeholders, and drive informed technology decisions.
Collaborate across product and platform teams to maximize reuse, standardization, and platform adoption while minimizing redundant solutions.
Lead technical planning and governance across multiple squads, ensuring consistency in architecture, engineering practices, scalability, and maintainability.
Drive adoption of modern engineering practices that improve reliability, performance, security, observability, and delivery quality.
Own technology lifecycle management, including APIs, data interfaces, integration patterns, and platform modernization initiatives.
Research emerging technologies and industry trends, conducting innovation spikes and pilots to identify opportunities for business value.
Support Product Owners and Lead Engineers in backlog prioritization and technical debt management.
Mentor engineers and technical leaders while fostering a culture of engineering excellence, innovation, collaboration, and continuous learning.
Manage technical risks, dependencies, impediments, and cross-product integration challenges.
Serve as the primary technical point of contact for ISRM, Quality and Compliance (Q-CSV), vendors, enterprise platform teams, and external partners.
AI, GenAI & Agentic AI Leadership
Lead the design and implementation of enterprise Generative AI and Agentic AI solutions leveraging Azure OpenAI, Azure AI Services, Microsoft Fabric AI capabilities, Databricks AI/ML, and enterprise knowledge platforms.
Design and operationalize scalable Retrieval-Augmented Generation (RAG) solutions, vector databases, semantic search capabilities, knowledge graphs, and enterprise document intelligence platforms.
Build and govern AI-ready data products that provide trusted, secure, and contextualized data for AI applications.
Establish standards for prompt engineering, model evaluation, observability, guardrails, responsible AI, performance monitoring, and AI lifecycle management (LLMOps/MLOps).
Develop scalable AI data pipelines supporting embedding creation, vectorization, document ingestion, multimodal processing, and unstructured data management.
Partner with business, analytics, digital, and experience teams to identify and prioritize high-impact AI use cases that drive measurable business outcomes.
Ensure AI solutions comply with enterprise security, privacy, governance, regulatory, and responsible AI requirements.
Evaluate emerging foundation models, copilots, AI agents, and AI platforms, driving adoption where business value can be demonstrated.
Technical Scope & Expectations (Data Engineering Focus)
Lead the design, development, and implementation of enterprise-scale data products, data platforms, and data pipelines using Azure, Microsoft Fabric, Databricks, and modern cloud-native architectures.
Design and implement Lakehouse, Warehouse, Data Product, and Data Sharing architectures supporting enterprise analytics and AI workloads.
Enable governed analytics and semantic modeling practices through Power BI, including performance optimization, security, scalability, and model governance.
Apply Data Mesh principles and federated governance approaches to support domain-driven ownership and scalable enterprise data ecosystems.
Establish and enforce enterprise-wide data modeling standards, including conceptual, logical, physical, dimensional, canonical, and domain-oriented data models.
Implement robust data governance practices, including data q