Description
The Director of Engineering for the Enterprise Data Platform will provide unified technical leadership to modernize, integrate, and optimize Salesforce's foundational enterprise data ecosystem. This platform is mission-critical for enabling AI innovation, advanced analytics, and trusted self-service data access at scale across the company. In this critical role, you will bridge the world of modern analytics with the cutting edge of semantic AI. You will manage the core data backbone of the company using technologies such as Snowflake, dbt, Informatica and Airflow —while simultaneously building and scaling our advanced Knowledge Graph Platform (Neo4j & TopQuadrant). Your mission is to create a seamless fabric where structured data flows effortlessly into high-value knowledge graphs to power BI, Advanced Analytics, and Generative AI.
Strategic Leadership & Platform Vision
Own and champion the technical vision and roadmap for the Enterprise Data Platform Foundation.
Align platform engineering priorities with strategic company goals, particularly AI enablement, analytics modernization, and data-driven decision-making.
Operationalize a data mesh architecture to promote domain-oriented ownership, interoperability, and AI/ML enablement.
Champion the implementation of a unified governance and operating model across a complex technology stack (including Salesforce Data Cloud, Snowflake, dbt Cloud, Airflow, Informatica, Tableau, and AWS).
Ensure every piece of infrastructure—from Snowflake RBAC roles to Neo4j clusters and Airflow workers—is defined in code (Terraform/Helm) and version controlled.
Knowledge Graph & Semantic Strategy
Lead the implementation of Neo4j as a high-performance serving layer. Design pipelines that project data from Snowflake into the Graph for connected analysis.
Drive the adoption of TopQuadrant (TopBraid EDG) to govern business glossaries and ontologies, ensuring our graph data is semantically rich and compliant.
Architect the "Graph RAG" capability where the structured data in Snowflake and the connected data in Neo4j connect to LLM agents via a unified API.
Engineering Standards & Leadership
DevOps Culture: Enforce strict software lifecycles for data. Branches, pull requests, unit tests (for data and code), and automated deployments are mandatory.
Reliability Engineering (SRE): Own the uptime. Implement robust monitoring, alerting, and incident response processes for the platform.
Vendor management: Manage vendor relationships and strategically optimize licensing, infrastructure, and operational spending.
Team Growth: Recruit and lead backend engineers, data engineers, and graph specialists. Foster a team culture that values clean code, architectural reviews, and technical fearlessness.
People Leadership
Build, lead, and mentor high-performing engineering teams across multiple disciplines.
Cultivate a collaborative culture, fostering strong cross-functional partnerships with Product, Security, Data Governance, and Business Units.
Manage a diverse team of Software Engineers (Snowflake/Airflow/dbt focus), and Graph Engineers (Neo4j focus).
What We’re Looking For:
5+ years managing software engineering teams with a focus on backend or data infrastructure.
You started your career as a Software Engineer. You are comfortable reviewing Python/Java code, discussing API latency, and designing microservices.
A proven track record of delivering scalable, secure, and cost-optimized infrastructure solutions.
Deep expertise in enabling AI/ML and advanced analytics through large-scale data platform modernization.
Hands-on expertise with the core technology stack: Snowflake, dbt Cloud, Airflow, Informatica IICS, Tableau, and Salesforce Data Cloud.
Experience with Apache Spark, Apache Iceberg, and Python for large-scale data processing.
Deep understanding of how distributed databases work. You know why a graph database (Neo4j) outperforms a relational one (Snowflake) for certain queries and vice versa.
Production experience with Snowflake, dbt, and Airflow, managed via Terraform and CI/CD.
Proven experience implementing and integrating AI-assisted developer tools (e.g., Cursor, MCP, Copilot).
Strong background in platform operations, unified governance, and rigorous SLA management.
Experience with cloud-native architectures (AWS, GCP, Azure) and hybrid deployments, ideally with a background in DevOps, MLOps, or DataOps.
Exceptional communication skills with the ability to influence executive leadership and technical audiences.
Demonstrated success in change management within a complex, matrixed organization.
*LI-Y
For roles in San Francisco and Los Angeles: Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.