Johnson & Johnson is a leader in healthcare innovation, committed to building a world where complex diseases are effectively managed. They are seeking a Principal Data Engineer to own product engineering and architectural decisions, ensuring the successful delivery of scalable data and AI solutions while collaborating with various teams to solve complex engineering challenges.

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

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
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

Data Engineer

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