Principal Data Engineer

Job 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 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. 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. Responsibilities: Lead the end-to-end data integration strategy for the Butterfly program, ensuring seamless data movement across CRM, ERP, MDM, CDP, analytics, and downstream platforms. Own integration architecture decisions, standards, and patterns across batch, real-time, API-based, event-driven, and file-based integrations. Partner with business, product, and application teams to define data exchange requirements and align with enterprise data standards. Drive the design, development, testing, and deployment of scalable integration solutions supporting global releases and country rollouts. Establish and govern integration design reviews, technical specifications, mapping documents, and interface contracts. Coordinate cross-functional teams to manage integration dependencies, risks, and release readiness. Serve as the primary technical lead for application onboarding, source-to-target mapping, integration assessments, and data flow design. Ensure integration solutions meet performance, reliability, scalability, security, and compliance requirements. Technical Scope & Expectations (Data Engineering Focus). Design and implement enterprise integration solutions using APIs, ETL/ELT pipelines, messaging frameworks, event-driven architectures, and cloud-native integration patterns. Lead source system onboarding and integration of commercial, customer, product, consent, and transactional data into Butterfly data products. Establish reusable integration frameworks, canonical data models, and standardized mapping approaches to accelerate delivery and reduce complexity. Embed data quality controls, reconciliation processes, exception handling, and monitoring capabilities into all integration solutions. Collaborate with MDM, Data Product, Analytics, Experience, and Application teams to support a unified enterprise data ecosystem. Define integration observability standards, including logging, alerting, monitoring, SLA management, and operational support processes. Drive API-first integration strategies and support the governance and lifecycle management of enterprise APIs and data services. Lead migration and modernization efforts from legacy integrations to cloud-native architectures leveraging Azure, Databricks, and Microsoft Fabric. Key Skills (Workday “What You Bring”) Leadership Skills and Behaviors Creates a culture that relentlessly focuses on improving outcomes for customers, employees, and the communities we serve. Leads and influences technical teams across multiple squads, functions, geographies, and experience levels. Demonstrates commitment to Our Credo, Diversity, Equity & Inclusion by fostering an environment where diverse talent can thrive. Brings a strong customer-centric mindset and ensures the delivery of products that anticipate and address customer needs. Establishes trusted partnerships with architects, engineering leaders, product leaders, and business stakeholders. Coaches, develops, and mentors engineering talent while promoting accountability, ownership, and innovation. Product / Digital Expertise Extensive experience leading Agile delivery organizations, including product development, governance, standards, and organizational change management. Deep 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. 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. Domain Expertise Experience leading the 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. Required Qualifications 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. Preferred Qualifications Experience operating in highly regulated industries with stringent security, privacy, compliance, and quality requirements