Principal Software Engineer, Data Platform

Salesforce Salesforce · Enterprise · San Francisco, CA +2

Principal Software Engineer for Salesforce's Enterprise Data Platform, focusing on architecting and scaling the data ecosystem, integrating structured data into knowledge graphs, and powering BI, Advanced Analytics, and Generative AI. The role involves designing integration patterns for AI-assisted tooling, orchestrating AI agent systems, and contributing to shared system context for AI reliability. Key responsibilities include technical strategy, platform architecture, performance engineering, AI enablement, knowledge graph and semantic engineering (Graph RAG, semantic layer), and setting engineering standards. Requires deep backend distributed systems/data infrastructure experience, expertise in data platforms (Snowflake, dbt, Airflow), graph databases (Neo4j), cloud-native technologies, and AI/LLM integration (RAG, vector databases).

What you'd actually do

  1. Architect the Roadmap: Define the long-term technical architecture for the Enterprise Data Platform. Translate business strategy into technical specifications, ensuring our stack allows for "Data Mesh" scalability and domain-oriented ownership.
  2. Lead the technical design of "Graph RAG" (Retrieval-Augmented Generation), creating the patterns that allow LLM agents to query structured Snowflake data via the Neo4j Knowledge Graph.
  3. Design and orchestrate complex systems where AI agents integrate seamlessly into human workflows, driving efficiency and innovation at scale.
  4. Build and ship high-quality, production-grade software using modern engineering practices, with AI as a core part of your development workflow by pushing the boundaries of AI development tools to deliver secure, optimized, and high-quality code.
  5. Critically evaluate code (Human or AI-generated) for correctness, quality, security, and performance

Skills

Required

  • 10+ years of software engineering experience, with at least 5 years focused on backend distributed systems or data infrastructure at scale.
  • Expert coder (Python, Java, or Go)
  • Debugging distributed traces
  • Optimizing JVM heap
  • Rewriting slow SQL query plans
  • Designing large-scale data platforms
  • Understanding of CAP theorem, eventual consistency, and trade-offs between batch and streaming architectures.
  • Hands-on expert-level knowledge of Snowflake (internals/clustering), dbt (macro design/Jinja), Airflow (scheduler internals), and Tableau.
  • Deep understanding of Graph theory and implementation.
  • Modeling data in Neo4j (Cypher) to avoid super-node problems and optimize traversal performance.
  • Mastery of AWS/GCP services (IAM, VPC, PrivateLink, S3/GCS)
  • Container orchestration (Kubernetes/EKS)
  • Experience implementing RAG architectures, vector databases, or integrating LLMs into data platforms.

Nice to have

  • Terraform/Helm configurations
  • Data serialization formats (Parquet/Iceberg)
  • distributed compute costs across Snowflake and Spark
  • AI-assisted tooling (Cursor, MCP, Copilot)
  • Semantic governance layer (TopQuadrant/TopBraid EDG)
  • ontologies are mechanically enforced
  • Polyglot Persistence
  • Relational Store (Snowflake)
  • Graph Store (Neo4j)
  • high-velocity pipelines (Kafka/Airflow)
  • Code Quality & DevOps
  • CI/CD pipelines (unit testing data, schema validation)
  • Resiliency Architecture
  • monitoring and alerting frameworks (SRE)
  • Mentorship without Authority
  • design reviews, RFCs, and pair programming sessions.

What the JD emphasized

  • primary technical architect
  • technical "north star"
  • cutting edge of Semantic AI
  • advanced Knowledge Graph Platform
  • high-value knowledge graphs
  • power BI, Advanced Analytics, and Generative AI
  • AI-assisted tooling
  • AI as a core part of your development workflow
  • AI agents integrate seamlessly
  • shared system context, an explicit repository of system designs, constraints, and standards that enables AI to operate accurately and reliably
  • Graph RAG (Retrieval-Augmented Generation)
  • LLM agents to query structured Snowflake data via the Neo4j Knowledge Graph
  • AI/LLM Integration
  • Experience implementing RAG architectures, vector databases, or integrating LLMs into dat

Other signals

  • AI CRM
  • Agentforce
  • AI agents
  • Knowledge Graph Platform
  • Graph RAG
  • Generative AI