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 product ionize 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.

• 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, Lang Chain, 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, Git Hub Copilot, or MCP frameworks).
• Distributed Systems & Cloud:
  Proven experience building applications on cloud‑native systems (AWS, GCP, or Azure)…