{"id":2468664,"url":"https://alion.io/job/johnson-johnson-principal-data-engineer","title":"Principal Data Engineer","company":{"id":14152,"name":"Johnson & Johnson","domain":"jnj.com","url":"https://alion.io/company/jnj","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New Brunswick, United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":142000,"max_usd":254000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":86},"experience_years_min":15,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Embeddings","optional":false},{"name":"Fabric AI","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Microsoft Fabric","optional":false},{"name":"Multimodal AI","optional":false},{"name":"OpenAI","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Power BI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false}],"status":"live","first_seen_at":"2026-10-13T19:18:48Z","employer_posted_date":"2026-10-13","last_verified_at":"2026-10-14T01:31:14Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"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\nAs 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.\nJob Function:\nData Analytics & Computational SciencesJob Sub Function:\nData EngineeringJob Category:\nScientific/TechnologyAll Job Posting Locations:\nNew Brunswick, New Jersey, United States of AmericaJob Description:\nThis is a duration based role that will last 2 years.\nThe 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.\nThe 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.\nResponsibilities / Key Jobs to be Done (Workday “What You Will Do”)\nDefine 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.\nEnsure technical implementations align with enterprise architecture standards, business objectives, economic frameworks, and long-term technology strategy.\nPartner with business stakeholders to shape product vision, define capabilities, and translate business requirements into scalable data, analytics, and AI solutions.\nProvide hands-on technical leadership, actively contributing to architecture reviews, design reviews, code reviews, proof-of-concepts, and technical enablers.\nEvaluate solution alternatives, validate concepts with stakeholders, and drive informed technology decisions.\nCollaborate across product and platform teams to maximize reuse, standardization, and platform adoption while minimizing redundant solutions.\nLead technical planning and governance across multiple squads, ensuring consistency in architecture, engineering practices, scalability, and maintainability.\nDrive adoption of modern engineering practices that improve reliability, performance, security, observability, and delivery quality.\nOwn technology lifecycle management, including APIs, data interfaces, integration patterns, and platform modernization initiatives.\nResearch emerging technologies and industry trends, conducting innovation spikes and pilots to identify opportunities for business value.\nSupport Product Owners and Lead Engineers in backlog prioritization and technical debt management.\nMentor engineers and technical leaders while fostering a culture of engineering excellence, innovation, collaboration, and continuous learning.\nManage technical risks, dependencies, impediments, and cross-product integration challenges.\nServe as the primary technical point of contact for ISRM, Quality and Compliance (Q-CSV), vendors, enterprise platform teams, and external partners.\nAI, GenAI & Agentic AI Leadership\nLead 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.\nDesign and operationalize scalable Retrieval-Augmented Generation (RAG) solutions, vector databases, semantic search capabilities, knowledge graphs, and enterprise document intelligence platforms.\nBuild and govern AI-ready data products that provide trusted, secure, and contextualized data for AI applications.\nEstablish standards for prompt engineering, model evaluation, observability, guardrails, responsible AI, performance monitoring, and AI lifecycle management (LLMOps/MLOps).\nDevelop scalable AI data pipelines supporting embedding creation, vectorization, document ingestion, multimodal processing, and unstructured data management.\nPartner with business, analytics, digital, and experience teams to identify and prioritize high-impact AI use cases that drive measurable business outcomes.\nEnsure AI solutions comply with enterprise security, privacy, governance, regulatory, and responsible AI requirements.\nEvaluate emerging foundation models, copilots, AI agents, and AI platforms, driving adoption where business value can be demonstrated.\nTechnical Scope & Expectations (Data Engineering Focus)\nLead 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.\nDesign and implement Lakehouse, Warehouse, Data Product, and Data Sharing architectures supporting enterprise analytics and AI workloads.\nEnable governed analytics and semantic modeling practices through Power BI, including performance optimization, security, scalability, and model governance.\nApply Data Mesh principles and federated governance approaches to support domain-driven ownership and scalable enterprise data ecosystems.\nEstablish and enforce enterprise-wide data modeling standards, including conceptual, logical, physical, dimensional, canonical, and domain-oriented data models.\nImplement robust data governance practices, including data quality management, metadata management, lineage, master data integration, security, and access controls.\nDevelop scalable data architectures supporting both structured and unstructured data required for advanced analytics and AI applications.\nEnable interoperability and data product consumption through APIs, event-driven architectures, and enterprise integration patterns.\nAI Engineering Expertise\nDemonstrate deep, hands-on expertise with Large Language Models (LLMs), Generative AI, AI agents, embeddings, vector databases, prompt engineering, and enterprise AI architectures.\nDesign and operate enterprise AI engineering platforms, including MLOps, LLMOps, model governance, model evaluation frameworks, and responsible AI controls.\nArchitect and support AI-ready data foundations that enable trusted, scalable, and governed enterprise AI solutions.\nKey Skills (Workday “What You Bring”)\nLeadership Skills and Behaviors\nCreates a culture that relentlessly focuses on improving outcomes for customers, employees, and the communities we serve.\nLeads and influences technical teams across multiple squads, functions, geographies, and experience levels.\nDemonstrates commitment to Our Credo, Diversity, Equity & Inclusion by fostering an environment where diverse talent can thrive.\nBrings a strong customer-centric mindset and ensures the delivery of products that anticipate and address customer needs.\nEstablishes trusted partnerships with architects, engineering leaders, product leaders, and business stakeholders.\nCoaches, develops, and mentors engineering talent while promoting accountability, ownership, and innovation.\nProduct / Digital Expertise\nExtensive experience leading Agile delivery organizations, including product development, governance, standards, and organizational change management.\nDeep technical expertise across Azure, Microsoft Fabric, Databricks, Power BI, Data Mesh, Data Federation, Data Modeling, Data Governance, Enterprise Data Management, AI Engineering, and Generative AI technologies.\nStrong understanding of cloud-native architectures, API-first design, distributed systems, and enterprise integration patterns.\nExpertise with modern SDLC practices, CI/CD pipelines, test automation, DevOps, containerization, Infrastructure as Code, and platform engineering.\nProven ability to evaluate technical tradeoffs and guide teams toward scalable, maintainable solutions.\nDomain Expertise\nExperience leading the selection, implementation, integration, and operation of enterprise data, analytics, AI, and digital platforms.\nStrong experience managing products and platforms throughout their lifecycle in complex, multi-team environments.\nDeep understanding of enterprise delivery practices including planning, dependency management, governance, compliance, quality management, and operational excellence.\nAbility to align technology investments with business strategy, value drivers, and industry trends.\nDemonstrated success driving measurable business outcomes through data, analytics, and AI solutions.\nRequired Qualifications\nBachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related field; Master’s degree preferred.\n15+ years of experience in enterprise data engineering, technical architecture, analytics, AI platforms, and cloud engineering.\nProven experience leading architecture decisions, technical strategy, and engineering standards across multiple teams and products.\nHands-on expertise with Azure, Microsoft Fabric, Databricks, Power BI, and modern cloud-native technologies.\nStrong experience with Data Mesh, Data Federation, Data Products, Data Modeling, Data Governance, and Enterprise Data Management disciplines.\nDemonstrated expertise with Generative AI technologies including LLMs, RAG, vector databases, AI agents, prompt engineering, and enterprise AI architectures.\nExperience implementing MLOps, LLMOps, Responsible AI, model governance, and AI operationalization frameworks.\nStrong understanding of structured and unstructured data architectures supporting enterprise AI and intelligent automation.\nExceptional communication, collaboration, and stakeholder management skills with the ability to influence both technical and non-technical audiences.\nProven ability to lead through ambiguity and drive alignment across business and technology organizations.\nPreferred Qualifications\nExperience operating in highly regulated industries with stringent security, privacy, compliance, and quality requirements.\nExperience partnering with ISRM, Quality & Compliance organizations, and audit functions to ensure operational readiness and control compliance.\nExperience with enterprise knowledge management platforms, document intelligence solutions, and semantic technologies.\nFamiliarity with knowledge graphs, graph databases, vector platforms, enterprise search, and advanced AI retrieval strategies.\nDemonstrated experience establishing engineering observability, reliability engineering practices, Site Reliability Engineering (SRE), and operational excellence frameworks.\nExperience driving measurable business outcomes and value realization through enterprise data and AI initiatives.\nExperience leading enterprise AI transformation programs, AI platform strategy, and adoption of emerging AI technologies at scale.\nJohnson & Johnson is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, protected veteran status or other characteristics protected by federal, state or local law. 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