Principal Data Engineer (pmts) - Mdm

Salesforce Salesforce · Enterprise · San Francisco, CA +3

Salesforce is seeking a Principal Member of Technical Staff (PMTS) to lead the technical vision, architecture, and execution for their Enterprise Knowledge Graph platform. This role involves defining graph data models, ontologies, semantic layers, APIs, vector search, and retrieval architectures to support AI and agentic applications. The PMTS will also drive the strategy and productionization of AI-powered engineering tools and developer platforms, leveraging technologies like Claude and AI agents. The role requires deep expertise in Knowledge Graph technologies, graph databases, semantic modeling, and building scalable AI solutions, including RAG and agentic workflows, as well as AI-enabled developer tooling.

What you'd actually do

  1. Define and drive the long-term technical vision, architecture, and roadmap for Salesforce's Enterprise Knowledge Graph platform.
  2. Lead architecture and design for knowledge graph ecosystems, including graph data models, ontologies, semantic layers, entity resolution frameworks, graph APIs, vector search capabilities, and retrieval architectures supporting AI and agentic use cases.
  3. Establish enterprise standards, governance models, engineering patterns, and best practices for Knowledge Graph development, deployment, and lifecycle management.
  4. Define strategies for integrating structured, unstructured, and third-party data sources into graph-based platforms using scalable data engineering patterns.
  5. Partner with Architecture, Product, AI Platform, and Data Engineering organizations to align platform investments with enterprise priorities and future AI initiatives.

Skills

Required

  • 12+ years of experience in software engineering, data engineering, distributed systems, enterprise data platforms, or related technical domains.
  • A related technical degree required.
  • Deep expertise in Knowledge Graph technologies, ontology engineering, semantic modeling, linked data, graph databases, and enterprise metadata management.
  • Strong hands-on experience with graph technologies such as Neo4j, TopQuadrant, RDF/OWL, SPARQL, property graph models, semantic reasoning frameworks, or similar technologies.
  • Strong experience designing enterprise data engineering architectures, including large-scale ingestion, transformation, orchestration, metadata management, and data governance frameworks.
  • Experience with cloud-native architectures and platforms including AWS, GCP, or Azure.
  • Strong understanding of distributed systems, APIs, microservices

Nice to have

  • Knowledge Graph technologies
  • ontology engineering
  • semantic modeling
  • linked data
  • graph databases
  • enterprise metadata management
  • Neo4j
  • TopQuadrant
  • RDF/OWL
  • SPARQL
  • property graph models
  • semantic reasoning frameworks
  • graph-powered AI solutions
  • semantic retrieval systems
  • vector search platforms
  • RAG architectures
  • agentic workflows
  • AI-powered developer tools
  • engineering platforms
  • automation solutions
  • Claude
  • Cursor
  • Windsurf
  • GitHub Copilot
  • AI agents
  • MCP frameworks
  • enterprise data engineering architectures
  • large-scale ingestion
  • transformation
  • orchestration
  • metadata management
  • data governance frameworks
  • cloud-native architectures
  • AWS
  • GCP
  • Azure
  • distributed systems
  • APIs
  • microservices

What the JD emphasized

  • Proven experience defining and delivering enterprise-scale Knowledge Graph platforms supporting AI, semantic search, data integration, and agentic applications.
  • Proven experience leading the architecture and implementation of graph-powered AI solutions, semantic retrieval systems, vector search platforms, RAG architectures, and agentic workflows.
  • Demonstrated success in building, scaling, and productionizing AI-powered developer tools, engineering platforms, or automation solutions using technologies such as Claude, Cursor, Windsurf, GitHub Copilot, AI agents, MCP frameworks, or similar ecosystems.

Other signals

  • Enterprise Knowledge Graph platform to power AI-driven experiences
  • AI-powered developer productivity solutions
  • agentic AI use cases
  • AI-powered engineering tools and developer platforms