Principal Data Engineer

Johnson & Johnson•Brunswick, OH

2d•Remote

About The Position

The Principal Data Engineer- AI owns product engineering and architectural decisions, serving as the technical visionary and hands-on leader responsible for solution delivery. The role partners with Product Owners, Product Group Engineers, Lead Engineers, architects, business stakeholders, and cross-functional product squads to solve complex engineering challenges, define scalable technical solutions, and align delivery with enterprise technology strategy. It is accountable for product technical architecture, engineering standards, technology roadmaps, and successful delivery of scalable, secure, and governed data and AI solutions. This is a two-year duration-based role that can be fully remote. The role leads enterprise data engineering and AI architecture, including data products, pipelines, platforms, lakehouse and warehouse architectures, governed analytics, data modeling, data governance, enterprise integration, and AI-ready data foundations. It also leads the design and operationalization of Generative AI and Agentic AI solutions, including RAG, vector databases, semantic search, document intelligence, AI pipelines, model evaluation, responsible AI controls, and LLMOps/MLOps. The position mentors engineering talent, manages technical risks and dependencies, and serves as a technical contact for ISRM, Quality and Compliance, vendors, platform teams, and external partners.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related field.
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.
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 technical and non-technical audiences.
Proven ability to lead through ambiguity and drive alignment across business and technology organizations.
Extensive experience leading Agile delivery organizations, including product development, governance, standards, and organizational change management.
Strong understanding of cloud-native architectures, API-first design, distributed systems, and enterprise integration patterns.
Expertise with modern SDLC practices, CI/CD pipelines, test automation, DevOps, containerization, Infrastructure as Code, and platform engineering.
Proven ability to evaluate technical tradeoffs and guide teams toward scalable, maintainable solutions.
Experience leading selection, implementation, integration, and operation of enterprise data, analytics, AI, and digital platforms.
Strong experience managing products and platforms throughout their lifecycle in complex, multi-team environments.
Deep understanding of enterprise delivery practices, including planning, dependency management, governance, compliance, quality management, and operational excellence.
Ability to align technology investments with business strategy, value drivers, and industry trends.
Demonstrated success driving measurable business outcomes through data, analytics, and AI solutions.

Nice To Haves

Master’s degree.
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 product engineering strategy, architecture, roadmap, and technical direction in partnership with product and business leaders.
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 requirements into scalable data, analytics, and AI solutions.
Provide hands-on technical leadership through architecture and 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 increase reuse, standardization, and platform adoption while minimizing redundant solutions.
Lead technical planning and governance across multiple squads, ensuring consistent architecture, engineering practices, scalability, and maintainability.
Drive 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 business opportunities.
Support Product Owners and Lead Engineers with backlog prioritization and technical debt management.
Mentor engineers and technical leaders, fostering 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 using Azure OpenAI, Azure AI Services, Microsoft Fabric AI capabilities, Databricks AI/ML, and enterprise knowledge platforms.
Design and operationalize scalable RAG solutions, vector databases, semantic search, 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 for 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 with measurable business outcomes.
Ensure AI solutions meet 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 design, development, and implementation of enterprise-scale data products, platforms, and pipelines using Azure, Microsoft Fabric, Databricks, and modern cloud-native architectures.
Design and implement Lakehouse, Warehouse, Data Product, and Data Sharing architectures for enterprise analytics and AI workloads.
Enable governed analytics and semantic modeling through Power BI, including performance optimization, security, scalability, and model governance.
Apply Data Mesh principles and federated governance approaches for domain-driven ownership and scalable enterprise data ecosystems.
Establish and enforce enterprise data modeling standards, including conceptual, logical, physical, dimensional, canonical, and domain-oriented models.
Implement data governance practices for data quality, metadata, lineage, master data integration, security, and access controls.
Develop scalable data architectures for structured and unstructured data used in advanced analytics and AI applications.
Enable interoperability and data product consumption through APIs, event-driven architectures, and enterprise integration patterns.
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 for trusted, scalable, and governed enterprise AI solutions.

Benefits

Medical insurance
Dental insurance
Vision insurance
Life insurance
Short- and long-term disability insurance
Business accident insurance
Group legal insurance
Pension plan
401(k) savings plan
Vacation: 120 hours per calendar year
Sick time: 40 hours per calendar year; 56 hours per calendar year for employees residing in Washington State
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 a child’s birth, adoption, or foster care placement
Condolence leave: 30 days for an immediate family member and 5 days for an extended family member
Caregiver leave: 10 days
Volunteer leave: 4 days
Military spouse time-off: 80 hours