Technical Product Owner - Lead Data AI Engineer

Johnson & Johnson Johnson & Johnson · Pharma · Milano, Italy +2

The Lead AI Data Engineer & Technical Product Owner is responsible for designing, building, and scaling data and AI engineering capabilities for enterprise AI, Generative AI, and Agentic AI solutions. This role combines Data Engineering, Cloud Data Platforms, MLOps, and AI operationalization with Agile delivery leadership and Technical Product Ownership. The position owns the end-to-end delivery of AI data foundations, from ingestion and transformation through feature engineering, model deployment enablement, monitoring, and operational support, ensuring alignment with enterprise standards.

What you'd actually do

  1. Act as Technical Product Owner (TPO) for AI Data Engineering products, capabilities, and platforms.
  2. Design, develop, and maintain scalable data pipelines supporting AI, Analytics, Machine Learning, and Generative AI use cases.
  3. Enable AI solution delivery through feature stores, vector databases, model deployment pipelines, and data services.
  4. Design and operate cloud-native AI and Data Platforms.
  5. Lead Agile squads delivering AI Data Engineering and platform capabilities.

Skills

Required

  • Bachelor's or Master's degree in Computer Science, Engineering, Data Engineering, Information Systems, Artificial Intelligence, or related disciplines.
  • 5–7+ years of experience in Data Engineering, AI Engineering, Platform Engineering, or Cloud Data Platform roles.
  • Proven experience designing enterprise-scale data pipelines and cloud-native data platforms.
  • Experience acting as Technical Product Owner, Delivery Lead, Lead Engineer, or Squad Lead.
  • Strong expertise in ETL/ELT, Data Lakes, Lakehouse architectures, Data Warehousing, Metadata Management, and Data Governance.
  • Hands-on experience with Azure, AWS, or GCP.
  • Understanding of Generative AI, LLMs, Vector Databases, RAG, and AI agent architectures.
  • Experience implementing MLOps, CI/CD, Infrastructure-as-Code, and DataOps practices.
  • Strong SQL and Python skills.
  • Experience working in Agile and Scrum environments.

Nice to have

  • Experience with Microsoft Fabric, Azure AI Foundry, Snowflake, or equivalent platforms.
  • Experience implementing vector search, semantic retrieval, and AI-ready data architectures.
  • Cloud, Data Engineering, Agile, Product Ownership, or AI certifications

What the JD emphasized

  • enterprise AI
  • Generative AI
  • Agentic AI
  • AI Data Engineering
  • data foundations
  • feature engineering
  • model deployment enablement
  • monitoring
  • operational support
  • enterprise architecture
  • security
  • compliance
  • data governance
  • data pipelines
  • Generative AI use cases
  • batch, streaming, and real-time data integration
  • governed data assets
  • enterprise AI use cases
  • data quality
  • observability
  • lineage
  • monitoring
  • reliability
  • secure
  • scalable
  • resilient
  • compliant
  • enterprise standards
  • feature stores
  • vector databases
  • model deployment pipelines
  • data services
  • Generative AI
  • LLM
  • RAG
  • Agentic AI architectures
  • scalable data foundations
  • AI Engineers
  • Data Scientists
  • operationalize AI solutions
  • MLOps
  • DataOps practices
  • cloud-native AI and Data Platforms
  • data ingestion
  • transformation
  • storage
  • governance
  • consumption
  • platform performance
  • scalability
  • reliability
  • cost efficiency
  • Infrastructure-as-Code
  • CI/CD
  • monitoring
  • observability frameworks
  • Agile squads
  • AI Data Engineering
  • platform capabilities
  • sprint planning
  • backlog refinement
  • technical reviews
  • delivery governance
  • DevOps
  • DataOps
  • Agile engineering best practices
  • business stakeholders
  • AI teams
  • platform teams
  • architects
  • delivery organizations
  • enterprise security
  • privacy
  • data governance
  • responsible AI requirements
  • OKRs/KPIs
  • platform adoption
  • data quality
  • delivery velocity
  • operational performance
  • business value realization
  • Data Engineering
  • AI Engineering
  • Platform Engineering
  • Cloud Data Platform
  • enterprise-scale data pipelines
  • cloud-native data platforms
  • Technical Product Owner
  • Delivery Lead
  • Lead Engineer
  • Squad Lead
  • ETL/ELT
  • Data Lakes
  • Lakehouse architectures
  • Data Warehousing
  • Metadata Management
  • Data Governance
  • Azure, AWS, or GCP
  • Generative AI
  • LLMs
  • Vector Databases
  • RAG
  • AI agent architectures
  • MLOps
  • CI/CD
  • Infrastructure-as-Code
  • DataOps practices
  • SQL
  • Python
  • Agile
  • Scrum
  • Microsoft Fabric
  • Azure AI Foundry
  • Snowflake
  • vector search
  • semantic retrieval
  • AI-ready data architectures
  • Cloud, Data Engineering, Agile, Product Ownership, or AI certifications

Other signals

  • designing, building, and scaling the data and AI engineering capabilities that power enterprise AI, Analytics, Generative AI, and Agentic AI solutions
  • owns the end-to-end delivery of AI data foundations, from ingestion and transformation through feature engineering, model deployment enablement, monitoring, and operational support
  • Enable AI solution delivery through feature stores, vector databases, model deployment pipelines, and data services
  • Support implementation of Generative AI, LLM, RAG, and Agentic AI architectures through scalable data foundations