Data Engineer – SMTS, LMTS, Knowledge Graph, AI

Salesforce

Data Engineer specializing in building Salesforce's Enterprise Knowledge Graph platform components. Collaborating on AI-driven experiences and leading technical execution with strong backend coding skills.

Posted 6/20/2026full-timeSan Francisco • California, New York, Washington • 🇺🇸 United StatesSeniorLead💰 $148,500 - $260,100 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsGoogle Cloud PlatformGRPCKafkaMicroservicesNeo4jPython

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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, LangChain, 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, GitHub 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.

Benefits

Comp & perks

time off programs
medical, dental, vision
mental health support
paid parental leave
life and disability insurance
401(k)
employee stock purchasing program

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

Pythongraph data modelscomplex graph queriesdata pipelinessemantic routing frameworksAI-powered developer toolsengineering automation platformscloud-native systemsmicroservicesevent-driven data streaming

Soft Skills

technical guidancementorshipcollaborationcode reviewsadherence to engineering standardsoperational best practices

Certifications

related technical degree