Director of Data Engineering, Salesforce

• 15+ years of experience in data or platform engineering, with 5+ years leading engineering managers and multi-team organizations
• Proven track record building and scaling high-performing engineering orgs in complex, cross-functional environments
• Deep expertise with Spark, Trino/Presto, dbt, Snowflake, and modern lakehouse architectures
• Experience with streaming systems (Flink, Kafka), including topic design, partitioning, and scaling
• Strong command of semantic layers, data modeling, and enterprise metrics systems
• Experience with AWS cloud infrastructure (S3, EMR, ECS, IAM) and containerized environments
• Executive-level communication skills — able to influence without authority and present at the VP/C-suite level
• A related technical degree required

Desirable

• We're building data products that will define Salesforce's next era of agentic intelligence — powering smarter, adaptive, and self-optimizing product experiences at enterprise scale
• As Director of Data Engineering, you'll set the strategic direction, architectural vision, and organizational execution for the data systems behind Customer Success Score (CSS), one of Salesforce's most critical product intelligence assets
• This is a high-visibility leadership role for a strategic technologist who thrives at the intersection of data, AI, and platform engineering
• Own the roadmap for the CSS data platform, aligning engineering investment with Salesforce's agentic and AI-first product strategy
• Establish architectural principles and governance standards for telemetry, semantic modeling, and metadata-driven discovery at enterprise scale
• Drive convergence across product analytics, ML infrastructure, and AI data foundations — breaking down silos and creating shared organizational leverage
• Represent data engineering at the executive level; shape organizational priorities and secure resources for strategic initiatives
• Set and uphold the technical bar for distributed data systems — including fault-tolerant batch and streaming architectures (Spark, Trino, Flink, Kafka, dbt, Snowflake)
• Define engineering standards across software quality, CI/CD, observability, and reliability
• Guide platform evolution to support autonomous agent reasoning, real-time adaptive decisioning, and AI-native product experiences
• Champion semantic consistency, metric governance, and trusted signal definition across the organization
• Evaluate emerging technologies and drive adoption decisions that extend the platform's strategic value
• Lead and grow multiple engineering teams, including managers and senior individual contributors
• Build a culture of ownership, psychological safety, and high accountability
• Drive succession planning, leadership development, and talent retention
• Define hiring strategy and org structure to scale with business needs
• Build deep partnerships with product, data science, AI platform, telemetry engineering, and infrastructure leaders
• Communicate architecture, trade-offs, and investment decisions clearly to VP and C-suite stakeholders
• Align technical execution with business outcomes and enterprise priorities
• Influence Salesforce-wide standards for data and AI engineering beyond your direct scope