Principal Software Engineer, Data Platform

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts. Job Category Software Engineering Job Details About Salesforce Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all. Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce. Principal Software Engineer, Data Platform The Principal Member of Technical Staff for the Enterprise Data Platform is the primary technical architect responsible for modernizing, integrating, and optimizing Salesforce's foundational data ecosystem. You will serve as the technical north star for the engineering teams, bridging the world of modern distributed analytics with the cutting edge of Semantic AI. In this high-impact individual contributor role, you will architect the backbone of the company using technologies such as Snowflake, dbt, Informatica, and Airflow —while simultaneously designing and scaling our advanced Knowledge Graph Platform (Neo4j & TopQuadrant) . Your mission is to design the paved path where structured data flows effortlessly into high-value knowledge graphs to power BI, Advanced Analytics, and Generative AI. You will not just oversee the architecture; you will write the proof-of-concepts, define the code standards, and solve the most complex scalability challenges. Key Responsibilities Technical Strategy & Platform Architecture 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. Infrastructure as Code (IaC) Evangelism: personally architect and review the Terraform/Helm configurations that define our infrastructure. Ensure that from Snowflake RBAC to Neo4j clusters, our platform is immutable, version-controlled, and reproducible. Performance Engineering: Deep dive into the hardest performance bottlenecks. Optimize query planners, data serialization formats (Parquet/Iceberg), and distributed compute costs across Snowflake and Spark. AI Enablement: Design the integration patterns for AI-assisted tooling (Cursor, MCP, Copilot) within the developer workflow to step-change developer velocity. Knowledge Graph & Semantic Engineering Graph RAG Architecture: 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. Semantic Layer Design: Design the integration between the physical data layer (Snowflake) and the semantic governance layer (TopQuadrant/TopBraid EDG), ensuring ontologies are mechanically enforced rather than theoretically defined. Polyglot Persistence: define the specific architectural patterns for when data should reside in a Relational Store (Snowflake) versus a Graph Store (Neo4j), and design the high-velocity pipelines (Kafka/Airflow) that keep them in sync. Engineering Standards & Technical Influence Code Quality & DevOps: Set the standard for code quality. You will be expected to code, review Pull Requests, and enforce strict CI/CD pipelines (unit testing data, schema validation). Resiliency Architecture: Design self-healing systems. Architect the monitoring and alerting frameworks (SRE) that ensure 99.9% availability for critical pipelines. Mentorship without Authority: Act as a technical mentor to Senior and Lead engineers across multiple squads. Ele ... (truncated, view full listing at source)