Data Engineer (SMTS/LMTS), Salesforce

• SMTS
• Experience: 8+ years of hands-on software engineering experience in development, data engineering, distributed systems, or enterprise data platforms
• Education: A related technical degree required
• Core Programming: Expert-level coding skills in backend ecosystems, with strong fluency in Python and standard object-oriented/functional programming languages
• Semantic Routing & AI: Hands-on experience developing and deploying custom semantic routers using Python (leveraging native embeddings, LangChain, or mathematical logic like cosine similarity) alongside RAG architectures, vector search platforms, and AI workflows
• Graph & Ontology Fundamentals: Solid experience working with graph databases and semantic web concepts (e.g., Neo4j, RDF/OWL, SPARQL, property graphs) and mapping data to structured taxonomies
• Developer Tooling: Practical experience configuring, testing, or integrating AI-assisted engineering tools or automation workflows (e.g., Claude, Cursor, Windsurf, GitHub Copilot, or MCP frameworks)
• Distributed Systems & Cloud: Proven experience building applications on cloud-native systems (AWS, GCP, or Azure) utilizing microservices, REST/gRPC APIs, and event-driven data streaming (e.g., Kafka)
• Delivery: Track record of owning and successfully delivering complex features in an agile, production-scale environment
• LMTS
• Experience: 10+ years of hands-on experience in software engineering, data engineering, distributed systems, or enterprise data platforms
• Ontology & Graph Expertise: Solid, hands-on experience designing and building Knowledge Graph platforms, formal ontologies, semantic models, taxonomies, or enterprise metadata management systems
• Tooling & Ecosystems: Strong hands-on experience with graph technologies and ontology engineering tools (e.g., Neo4j, TopQuadrant, Protégé, RDF/OWL, SPARQL, SHACL, property graphs) and semantic reasoning frameworks
• AI & Retrieval: Proven experience implementing graph-powered AI solutions, vector search platforms, Retrieval-Augmented Generation (RAG) architectures, and orchestrating agentic workflows
• Semantic Routing Mastery: Demonstrated hands-on experience designing, optimizing, and productionizing custom semantic routers using Python (leveraging native embeddings, LangChain, semantic-router, or specialized mathematical logic like cosine similarity) to decouple intent handling from expensive LLM calls
• Developer Automation: Experience deploying and integrating AI-assisted engineering tools or automation workflows using ecosystems like Claude, Cursor, Windsurf, GitHub Copilot, or MCP frameworks
• Backend & Cloud: Strong experience with cloud-native system designs (AWS, GCP, or Azure), distributed systems, microservices, and high-throughput event-driven systems
• Leadership: Demonstrated experience leading feature teams, guiding technical execution, and mentoring mid-to-senior level engineers

Desirable

• Salesforce is building the next-generation Enterprise Knowledge Graph platform to power AI-driven experiences, agentic applications, semantic search, enterprise data discovery, and intelligent decision-making across the company. The platform serves as the foundational knowledge layer connecting enterprise data, business entities, ontologies, and relationships across multiple domains
• We are seeking both a Senior Member of Technical Staff (SMTS) and a Lead Member of Technical Staff (LMTS) to join our Enterprise Knowledge Graph and AI Engineering team
• The SMTS will serve as a senior engineer and core systems developer — heavily hands-on, developing, optimizing, and scaling core knowledge graph components, semantic pipeline workflows, and AI-powered frameworks. You will partner with Lead and Principal Engineers to implement technical designs and build production-ready scalable systems that support agentic AI use cases across the enterprise
• The LMTS will serve as a hands-on technical lead, systems designer, and ontology engineer — designing, building, and scaling core knowledge graph infrastructure, semantic schemas, and AI-powered developer frameworks. You will partner closely with Principal Engineers, Product Management, Ontology experts, and Data Engineering teams to turn high-level engineering visions into production-ready scalable foundations
• Both roles will actively implement and drive AI-powered engineering tools and developer platforms that improve engineering productivity, software quality, and delivery velocity across the organization
• Design & Implement: Build and scale Salesforce's Enterprise Knowledge Graph platform components, focusing on performance, data throughput, system reliability, high availability, and robust data integrity. (LMTS: Lead hands-on design and implementation of platform subsystems; SMTS: Write high-quality, production-grade code.)
• Graph & Ontology Engineering: Develop graph data models, write complex graph queries, and construct scalable data pipelines to ingest and map structured and unstructured data to enterprise ontologies and taxonomies. (LMTS: Also design enterprise ontologies, taxonomies, semantic layers, entity resolution frameworks, graph APIs, and vector search capabilities to support advanced RAG and agentic workflows.)
• Semantic Routing: Write and maintain Python-based semantic routing frameworks to parse, classify, and dynamically direct incoming queries to the appropriate knowledge graph indexes or vector databases. (LMTS: Design, optimize, and productionize routing frameworks at enterprise scale, steering queries to appropriate knowledge graphs, ontology sub-graphs, or vector databases.)
• AI Tooling & Automation: Build, integrate, and leverage AI-powered developer tools and engineering automation platforms utilizing ecosystems such as Claude, Cursor, Windsurf, AI Agents, and Model Context Protocol (MCP) frameworks. (LMTS: Also develop, deploy, and optimize these tools; drive strategy and productionization.)
• Data Integration: Build scalable data pipelines and engineering patterns to ingest, transform, and orchestrate structured, unstructured, and third-party data sources into graph-based platforms mapped tightly to enterprise ontologies
• Feature Ownership & Technical Execution: Own the technical execution of specific platform features from concept through design, coding, testing, and production deployment. (LMTS: Also translate high-level technical visions and roadmaps into concrete system blueprints, ontology schemas, and execution plans.)
• Code Quality & Rigor: Participate heavily in code reviews, write comprehensive automated unit/integration tests, and ensure adherence to engineering standards and operational best practices
• Technical Mentorship: Provide technical guidance and mentorship to engineers on the team. (SMTS: Mentor MTS and Associate engineers. LMTS: Provide day-to-day guidance, code reviews, and design direction to SMTS, MTS, and associate engineers, fostering a culture of technical rigor and operational maturity.)
• Cross-Functional Collaboration: Work closely with Lead/Principal Engineers, Product Managers, and Data Engineering teams to deliver robust features aligned with broader enterprise AI priorities. (LMTS: Also partner with PMTS engineers and Ontology governance boards to ensure alignment with AI infrastructure standards.)
• Evaluate & Innovate (LMTS): Conduct deep-dive evaluations of emerging graph technologies, ontology modeling tools, semantic reasoning frameworks, vector databases, and AI tooling to continuously modernize the platform