Director, Data Engineering — Customer Success Score

Salesforce•Seattle, WA

1d•$197,300 - $344,700•Hybrid

About The Position

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. 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.

Requirements

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.

Nice To Haves

Experience with AI data engineering patterns, agentic data systems, or autonomous pipeline design
Familiarity with MCP, knowledge graphs, or modern metadata platforms
Experience designing programmatic data discovery and consumption frameworks

Responsibilities

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