Data Engineer – SMTS, LMTS, Knowledge Graph, AI

Responsibilities

• Build and scale Salesforce's Enterprise Knowledge Graph platform components, focusing on performance, data throughput, system reliability, high availability, and robust data integrity.
• 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.
• 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.
• 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.
• 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.
• Own the technical execution of specific platform features from concept through design, coding, testing, and production deployment.
• Participate heavily in code reviews, write comprehensive automated unit/integration tests, and ensure adherence to engineering standards and operational best practices.
• Provide technical guidance and mentorship to engineers on the team.
• Work closely with Lead/Principal Engineers, Product Managers, and Data Engineering teams to deliver robust features aligned with broader enterprise AI priorities.

• 8+ years of hands‑on software engineering experience in development, data engineering, distributed systems, or enterprise data platforms.
• A related technical degree required.
• Expert‑level coding skills in backend ecosystems, with strong fluency in Python and standard object‑oriented/functional programming languages.
• 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.
• Solid experience working with graph databases and semantic web concepts (e.g., Neo4j, RDF/OWL, SPARQL, property graphs) and mapping data to structured taxonomies.
• Practical experience configuring, testing, or integrating AI‑assisted engineering tools or automation workflows (e.g., Claude, Cursor, Windsurf, Git Hub Copilot, or MCP frameworks).
• 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).
• Track record of owning and successfully delivering complex features in an agile, production‑scale environment.