Principal Data Engineer – MDM

Jobtailor; California (MO); On-site; USD 180,000 - 240,000; Full time

Job summary

Salesforce is seeking a highly experienced Knowledge Graph Architect to define and drive the long-term technical vision, architecture, and roadmap for the Enterprise Knowledge Graph platform. You will lead architecture and design for graph ecosystems, establish standards and governance, and integrate data sources to support AI and enterprise use cases.

You will collaborate with Architecture, Product, AI Platform, and Data Engineering teams to align investments with enterprise priorities and

Qualifications

• 12+ years of experience in software engineering, data engineering, distributed systems, enterprise data platforms, or related technical domains.
• Proven experience defining and delivering enterprise-scale Knowledge Graph platforms supporting AI, semantic search, data integration, and agentic applications.
• 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.
• 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.
• 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, event-driven architectures, and modern software engineering practices.
• Demonstrated ability to influence senior technical leaders, executives, architects, and cross-functional stakeholders.
• Proven track record of defining technical strategy and driving execution across multiple teams and organizations.
• Excellent communication, leadership, and stakeholder management skills.

Responsibilities

• Define and drive the long-term technical vision, architecture, and roadmap for Salesforce's Enterprise Knowledge Graph platform.
• 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.
• Establish enterprise standards, governance models, engineering patterns, and best practices for Knowledge Graph development, deployment, and lifecycle management.
• Define strategies for integrating structured, unstructured, and third‑party data sources into graph-based platforms using scalable data engineering patterns.
• Partner with Architecture, Product, AI Platform, and Data Engineering organizations to align platform investments with enterprise priorities and future AI initiatives.
• Drive technical direction for semantic routing, graph‑powered retrieval, enterprise search, agent orchestration, and federated knowledge access patterns.
• Lead evaluation, selection, and adoption of graph technologies, semantic platforms, vector databases, and AI infrastructure required to support enterprise‑scale workloads.
• Define and drive the strategy for AI‑powered developer tooling, engineering automation, and productivity platforms that leverage technologies such as Claude, Cursor, Windsurf, AI Agents, MCP frameworks, and related AI ecosystems.
• Lead teams in productionizing AI‑enabled engineering solutions, ensuring scalability, security, governance, reliability, and measurable productivity improvements.
• Provide technical leadership and architectural guidance across PMTS, LMTS, SMTS, and contractor teams while driving alignment across multiple organizations.
• Serve as the primary technical authority for complex architectural decisions, platform investments, and long‑term engineering strategy.
• Foster innovation and continuous improvement while establishing a culture of engineering excellence, technical rigor, and operational maturity.

Skills

leadership

communication

stakeholder management

influence

innovation

continuous improvement

architectural guidance

collaboration

strategic thinking

Education

Related technical degree

Tools

Neo4j

TopQuadrant

RDF/OWL

SPARQL

semantic reasoning frameworks

Job description

Responsibilities

• Define and drive the long-term technical vision, architecture, and roadmap for Salesforce's Enterprise Knowledge Graph platform.
• 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.
• Establish enterprise standards, governance models, engineering patterns, and best practices for Knowledge Graph development, deployment, and lifecycle management.
• Define strategies for integrating structured, unstructured, and third‑party data sources into graph-based platforms using scalable data engineering patterns.
• Partner with Architecture, Product, AI Platform, and Data Engineering organizations to align platform investments with enterprise priorities and future AI initiatives.
• Drive technical direction for semantic routing, graph‑powered retrieval, enterprise search, agent orchestration, and federated knowledge access patterns.
• Lead evaluation, selection, and adoption of graph technologies, semantic platforms, vector databases, and AI infrastructure required to support enterprise‑scale workloads.
• Define and drive the strategy for AI‑powered developer tooling, engineering automation, and productivity platforms that leverage technologies such as Claude, Cursor, Windsurf, AI Agents, MCP frameworks, and related AI ecosystems.
• Lead teams in productionizing AI‑enabled engineering solutions, ensuring scalability, security, governance, reliability, and measurable productivity improvements.
• Provide technical leadership and architectural guidance across PMTS, LMTS, SMTS, and contractor teams while driving alignment across multiple organizations.
• Serve as the primary technical authority for complex architectural decisions, platform investments, and long‑term engineering strategy.
• Foster innovation and continuous improvement while establishing a culture of engineering excellence, technical rigor, and operational maturity.

Requirements

• 12+ years of experience in software engineering, data engineering, distributed systems, enterprise data platforms, or related technical domains
• A related technical degree required.
• Proven experience defining and delivering enterprise-scale Knowledge Graph platforms supporting AI, semantic search, data integration, and agentic applications.
• 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.
• 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.
• 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, event‑driven architectures, and modern software engineering practices.
• Demonstrated ability to influence senior technical leaders, executives, architects, and cross‑functional stakeholders.
• Proven track record of defining technical strategy and driving execution across multiple teams and organizations.
• Excellent communication, leadership, and stakeholder management skills.

Hard Skills

• knowledge graph technologies
• ontology engineering
• semantic modeling
• linked data
• graph databases
• enterprise metadata management
• data integration
• AI‑powered developer tools
• data engineering architectures
• cloud‑native architectures

Soft Skills

• leadership
• communication
• stakeholder management
• influence
• innovation
• continuous improvement
• technical authority
• architectural guidance
• collaboration
• strategic thinking