Data Engineer (SMTS/LMTS) - Knowledge Graph & AI

What You'll Actually Be Doing 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. 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. Education: A related technical degree required. 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. Even Better If SMTS Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field. Familiarity with ontology validation frameworks (e.g., SHACL) and data quality governance. Experience building integrations with data platform environments like Salesforce Data Cloud or enterprise CRM metadata architectures. Experience optimizing low‑latency applications and heavy‑throughput vector search lookups. Passion for engineering automation and driving personal/team velocity via advanced AI development tools. LMTS Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field with a focus on Semantic Web or Knowledge Representation. Direct experience integrating platforms with Salesforce Data Cloud, CRM platforms, or metadata‑driven system designs. Experience with semantic routing at enterprise scale, high‑throughput enterprise search systems, and graph‑powered recommendation engines. Deep familiarity with advanced ontology governance, federated knowledge management, and data contract alignment. Proven track record of optimizing engineering team velocity through the tailored implementation of AI developer tooling. Benefits Salesforce offers a variety of benefits to help you live well including: time off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program. More details about company benefits can be found at the following link: In the United States, compensation offered will be determined by factors such as location, job level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. The typical base salary range for this position is $148,500 - $260,100 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $178,900 - $285,800 annually. The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable. 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