Principal Data Engineer

Johnson & Johnson Innovative Medicine•New Brunswick, NJ

2d•$102,000 - $170,000•Onsite

About The Position

This is a duration based role that will last 2 years. The Principal Data Engineer 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.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related field; Master’s degree preferred.
15+ years of experience in enterprise data engineering, technical architecture, analytics, AI platforms, and cloud engineering.
Proven experience leading architecture decisions, technical strategy, and engineering standards across multiple teams and products.
Hands-on expertise with Azure, Microsoft Fabric, Databricks, Power BI, and modern cloud-native technologies.
Strong experience with Data Mesh, Data Federation, Data Products, Data Modeling, Data Governance, and Enterprise Data Management disciplines.
Demonstrated expertise with Generative AI technologies including LLMs, RAG, vector databases, AI agents, prompt engineering, and enterprise AI architectures.
Experience implementing MLOps, LLMOps, Responsible AI, model governance, and AI operationalization frameworks.
Strong understanding of structured and unstructured data architectures supporting enterprise AI and intelligent automation.
Exceptional communication, collaboration, and stakeholder management skills with the ability to influence both technical and non-technical audiences.
Proven ability to lead through ambiguity and drive alignment across business and technology organizations.

Nice To Haves

Experience operating in highly regulated industries with stringent security, privacy, compliance, and quality requirements.
Experience partnering with ISRM, Quality & Compliance organizations, and audit functions to ensure operational readiness and control compliance.
Experience with enterprise knowledge management platforms, document intelligence solutions, and semantic technologies.
Familiarity with knowledge graphs, graph databases, vector platforms, enterprise search, and advanced AI retrieval strategies.
Demonstrated experience establishing engineering observability, reliability engineering practices, Site Reliability Engineering (SRE), and operational excellence frameworks.
Experience driving measurable business outcomes and value realization through enterprise data and AI initiatives.
Experience leading enterprise AI transformation programs, AI platform strategy, and adoption of emerging AI technologies at scale.

Responsibilities

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.
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.
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 quality management, metadata management, lineage, master data integration, security, and access controls.
Develop scalable data architectures supporting both structured and unstructured data required for advanced analytics and AI applications.
Enable interoperability and data product consumption through APIs, event-driven architectures, and enterprise integration patterns.
Demonstrate deep, hands-on expertise with Large Language Models (LLMs), Generative AI, AI agents, embeddings, vector databases, prompt engineering, and enterprise AI architectures.
Design and operate enterprise AI engineering platforms, including MLOps, LLMOps, model governance, model evaluation frameworks, and responsible AI controls.
Architect and support AI-ready data foundations that enable trusted, scalable, and governed enterprise AI solutions.

Benefits

medical
dental
vision
life insurance
short- and long-term disability
business accident insurance
group legal insurance
consolidated retirement plan (pension)
savings plan (401(k))
Vacation –120 hours per calendar year
Sick time - 40 hours per calendar year
for employees who reside in the State of Washington –56 hours per calendar year
Holiday pay, including Floating Holidays –13 days per calendar year
Work, Personal and Family Time - up to 40 hours per calendar year
Parental Leave – 480 hours within one year of the birth/adoption/foster care of a child
Condolence Leave – 30 days for an immediate family member: 5 days for an extended family member
Caregiver Leave – 10 days
Volunteer Leave – 4 days
Military Spouse Time-Off – 80 hours