Principal Data Engineer

Salary: $102,000 - 170,000 per year Requirements:

• We require a bachelors degree in Computer Science, Engineering, Information Systems, Data Science, or a related discipline; a masters degree is preferred.
• We expect 15+ years of experience in enterprise data engineering, technical architecture, analytics, AI platforms, and cloud engineering.
• We need proven leadership in architecture decisions, technical strategy, and engineering standards across multiple teams and products.
• We require hands-on expertise with Azure, Microsoft Fabric, Databricks, Power BI, and modern cloud-native technologies.
• We expect strong experience with Data Mesh, Data Federation, Data Products, Data Modeling, Data Governance, and Enterprise Data Management.
• We need demonstrated expertise in Generative AI, including LLMs, RAG, vector databases, AI agents, prompt engineering, and enterprise AI architectures.
• We require experience implementing MLOps, LLMOps, Responsible AI, model governance, and AI operationalization frameworks.
• We expect a strong understanding of structured and unstructured data architectures that support enterprise AI and intelligent automation.
• We need exceptional communication, collaboration, and stakeholder management skills, with the ability to influence both technical and non-technical audiences.
• We require the ability to lead through ambiguity and drive alignment across business and technology organizations.

• We own the engineering strategy, technical architecture, roadmap, and overall technical direction for the product in partnership with product and architecture leaders.
• We ensure implementations align with enterprise standards, business goals, economic principles, and long-term technology strategy.
• We work with business stakeholders to shape product vision, define capabilities, and convert requirements into scalable data, analytics, and AI solutions.
• We provide hands-on technical leadership through architecture reviews, design reviews, code reviews, proof-of-concepts, and technical enablers.
• We assess solution options, validate concepts with stakeholders, and guide technology decisions.
• We collaborate across product and platform teams to drive reuse, standardization, and platform adoption while avoiding duplicate solutions.
• We lead technical planning and governance across multiple squads to maintain consistency in architecture, engineering practices, scalability, and maintainability.
• We promote modern engineering practices that strengthen reliability, performance, security, observability, and delivery quality.
• We manage technology lifecycle activities, including APIs, data interfaces, integration patterns, and platform modernization efforts.
• We research emerging technologies and industry trends, running innovation spikes and pilots to uncover business value.
• We support backlog prioritization and technical debt management.
• We mentor engineers and technical leaders while building a culture of excellence, innovation, collaboration, and continuous learning.
• We manage technical risks, dependencies, blockers, and cross-product integration challenges.
• We serve as the main technical contact for security, quality, compliance, vendors, enterprise platform teams, and external partners.
• We 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.
• We design and operationalize scalable retrieval-augmented generation, vector databases, semantic search, knowledge graphs, and enterprise document intelligence platforms.
• We build and govern AI-ready data products that deliver trusted, secure, and contextualized data for AI applications.
• We establish standards for prompt engineering, model evaluation, observability, guardrails, responsible AI, performance monitoring, and AI lifecycle management.
• We develop scalable AI data pipelines for embedding creation, vectorization, document ingestion, multimodal processing, and unstructured data management.
• We partner with business, analytics, digital, and experience teams to identify and prioritize high-impact AI use cases.
• We ensure AI solutions meet enterprise security, privacy, governance, regulatory, and responsible AI requirements.
• We evaluate emerging foundation models, copilots, AI agents, and AI platforms, adopting them where they create clear business value.
• We lead the design, development, and implementation of enterprise-scale data products, data platforms, and data pipelines using Azure, Microsoft Fabric, Databricks, and cloud-native architectures.
• We design and implement Lakehouse, Warehouse, Data Product, and Data Sharing architectures for analytics and AI workloads.
• We enable governed analytics and semantic modeling in Power BI, with attention to performance, security, scalability, and model governance.
• We apply Data Mesh principles and federated governance to support domain ownership and scalable enterprise data ecosystems.
• We establish and enforce enterprise data modeling standards across conceptual, logical, physical, dimensional, canonical, and domain-oriented models.
• We implement robust data governance practices, including data quality, metadata, lineage, master data integration, security, and access controls.
• We develop scalable data architectures that support both structured and unstructured data for advanced analytics and AI.
• We enable interoperability and data product consumption through APIs, event-driven architectures, and enterprise integration patterns.
• We apply deep, hands-on expertise in LLMs, Generative AI, AI agents, embeddings, vector databases, prompt engineering, and enterprise AI architectures.
• We design and run enterprise AI engineering platforms, including MLOps, LLMOps, model governance, model evaluation frameworks, and responsible AI controls.
• We architect and support AI-ready data foundations that enable trusted, scalable, and governed enterprise AI solutions.

• Agentic AI
• AI
• AI Agents
• Architect
• Azure
• Cloud
• Databricks
• Fabric
• Support
• LESS
• MLOps
• Power BI
• RAG
• Security
• API
• CI/CD
• DevOps

More:

We are Johnson & Johnson, a healthcare innovation company focused on preventing, treating, and curing complex diseases through smarter, less invasive, and more personal solutions across Innovative Medicine and MedTech. We are committed to an inclusive environment where every person is respected and valued for their individuality and contribution. This is a 2-year duration-based Principal Data Engineer role based in New Brunswick, New Jersey, where we offer a competitive base pay range of $102,000 to $170,000 along with a comprehensive benefits package that includes medical, dental, vision, life insurance, disability coverage, retirement and savings plans, and generous time-off benefits such as vacation, sick time, holidays, parental leave, caregiver leave, volunteer leave, and military spouse time off.

last updated 33 week of 2026