Principal Engineer, Data Engineering

Johnson & Johnson is a leader in healthcare innovation, committed to building a world where complex diseases are managed effectively. They are seeking a Principal Engineer in Data Engineering to lead the modernization of their commercial data platforms, enhancing intelligence and integration capabilities across the MedTech domain.

Responsibilities:

• Responsible for overall engineering and technology architecture definition, roadmap, and technical decisions for the product in coordination with the Product Owner, Product Group Engineer, and BU Architect; accountable for ensuring technical implementation aligns to BU architecture vision, economic framework, and business needs
• Work closely with business partners to help define product strategy/vision and translate business needs into scalable data engineering solutions
• Provide hands-on technical leadership (“hands-on-keys”) while supporting system/design/code reviews; assemble, write, and approve technical enablers as needed
• Drive development and selection of technical solution options and test concepts/potential solutions with users and stakeholders
• Negotiate technology options with business partners and collaborate with platform/product teams to identify opportunities to leverage platforms and drive reuse
• Manage development and review cadence; align the technical landscape across squads to promote reusability, scalability, and modularity
• Monitor product/platform developments and drive leading engineering practices for higher technical quality
• Own technology lifecycle management and the product API catalog, including definition, creation, and maintenance of technical interfaces (including data interfaces/APIs)
• Observe technology trends across the industry; conduct research spikes for new capability and innovation opportunities; ensure timely adoption of new technologies
• Help Product Owners/Lead Engineers prioritize technical backlogs across squads; coach and mentor product and squad members
• Manage risks; solve, escalate, and track cross-product dependencies, impediments, and delivery blockers
• Be the single point of contact for ISRM, TQ (Q-CSV), vendors, and enterprise platform owners for the product area
• Lead the design and implementation of enterprise data pipelines and data products using Azure and Microsoft Fabric (Lakehouse/Warehouse patterns as applicable), Databricks, and modern orchestration approaches
• Enable governed analytics consumption and semantic layer practices using Power BI (including performance, security, and model governance)
• Apply data mesh principles and data federation approaches to enable domain-aligned ownership with federated governance
• Establish strong data modeling standards (conceptual/logical/physical; dimensional and domain-oriented modeling) and ensure models are optimized for downstream consumption and interoperability
• Implement and enforce data governance, data quality controls, lineage/metadata practices, access controls, and data management standards

Requirements:

• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field (Master's preferred)
• 10+ years of experience in data engineering, data platforms, and/or technical architecture in an enterprise environment
• Demonstrated experience leading architecture decisions and engineering standards across multiple squads/teams
• Hands-on expertise with Azure and Microsoft Fabric, Databricks, and Power BI
• Strong experience with data mesh/data federation concepts, data modeling, data governance, and enterprise data management practices
• Proven ability to communicate complex technical concepts to both technical and non-technical stakeholders and drive alignment on decisions
• Experience in regulated and/or highly governed environments with strong security, privacy, and compliance requirements
• Experience partnering with ISRM and quality/compliance organizations (e.g., Q-CSV) on delivery and operational readiness
• Experience driving measurable outcomes, observability, reliability engineering practices, and operational excellence for data products