Description
About the Team
Data Platform Services (DPS) is the engineering heartbeat of Salesforce’s internal data ecosystem. We own the Enterprise Data Platform—the unified stack connecting Data 360 (Data Cloud), Snowflake, Tableau, and MuleSoft. Our mission is to accelerate Salesforce’s evolution into an Agent-first enterprise by building paved paths that allow teams to autonomously build trusted data products. We operate as Customer Zero, pushing our own technology to its absolute limits to prove that a converged, forward-looking architecture drives superior enterprise outcomes.
About the Role
We are seeking a visionary Sr. Director of Platform Engineering to lead the Data 360 (Data Cloud) and Visualization portfolios. This is a strategic software engineering role. You will be responsible for treating our internal data platform as a product, enforcing software engineering rigor (CI/CD, automated testing, Infrastructure-as-Code) to eliminate manual toil and fragility.
You will act as the lead for the Customer Zero vision, defining how Data360 should evolve to meet the extreme scale, security, and interoperability requirements of a large enterprise like Salesforce . You will bridge the gap between internal business requirements and the external product roadmap, ensuring that Salesforce Data360 is battle-tested and enterprise-ready.
Key Responsibilities
Platform Engineering & DevOps Maturity (CI/CD & Automation)
Automated SDLC: Lead the transition from manual, high-privilege production deployments to a fully automated CI/CD Platform Service. Architect pipelines that reduce deployment times from hours to minutes using modern DevOps tooling.
Test Automation Strategy: Enforce a "Test-Driven" culture for data products. Implement automated regression testing, data quality monitoring (Monte Carlo), and performance benchmarking to catch regressions before they hit production.
Infrastructure as Code (IaC): Treat infrastructure configuration (Data Spaces, Data Streams, Identity Resolution rules) as code. Ensure all environment configurations are version-controlled, reproducible, and drift-resistant.
Governance as Code: Embed governance policies (classification, PII detection, retention) directly into the deployment pipeline, ensuring compliance is automated rather than audited manually.
Data 360 Vision & Product Leadership (Customer Zero)
Product Evolution Partner: Act as the primary engineering counterpart to the Data Cloud Product (T&P) leadership. Use our internal implementation (E360/C360) to validate features like Vector Search, Hybrid Search, and BYOL at massive scale (600M+ records) before General Availability.
Enterprise Architecture Vision: Define the "North Star" architecture for Enterprise Data Cloud, specifically addressing High Availability (Tier 1a) and Disaster Recovery (15-minute RTO) requirements that enterprise customers demand.
Feedback Loops: Institutionalize structured feedback loops (QBRs, detailed bug logs, scale testing results) to directly influence the product roadmap, ensuring the platform supports complex enterprise use cases like Zero Copy and Data Mesh.
Visualization Platform Modernization
Tableau Cloud & Unified Analytics: Lead the engineering strategy for Tableau Next and Unified Analytics, harmonizing the semantic layer across CRM Analytics, Tableau, and Data Cloud to create a single source of truth for metrics.
Performance & Reliability: Work closely with the Ops Org to ensure that the platform is set up to support Tier 1 Service Level Objectives (SLOs) for the analytics platform (e.g., >99.9% uptime). Automate the detection and cleanup of unused content to optimize cost and performance.
Operational Excellence & Security
Tier 1a Mission Criticality: Work with Ops Org to Operationalize Data 360 as a Tier 1a platform capable of supporting 24/7 Agentic workloads. Establish a "Follow the Sun" SRE practice with a target Mean-Time-To-Detect (MTTD) of 15 minutes.
Zero Trust Security: Enforce a "Deny-by-Default" security posture. Implement Attribute-Based Access Control (ABAC) across all Data Spaces and achieve 100% stewardship and classification for high-risk assets.
What We’re Looking For
Engineering Leadership: 15+ years of experience leading platform engineering teams with a strong focus on DevOps, CI/CD, and Test Automation in complex data environments.
Salesforce Data Cloud & Tableau Expertise: Deep technical understanding of the Salesforce ecosystem, specifically Data Cloud architecture (DLOs, DMOs, Identity Resolution) and how to optimize it for scale.
Customer Zero Mindset: Proven ability to translate internal operational challenges into clear, actionable product requirements that shape external software roadmaps.
Modern Data Architecture: Experience operationalizing Data Mesh principles, Lakehouse patterns (Iceberg), and Zero-Copy architectures.
Change Agent: A track record of transforming "operations" teams into "platform engineering" teams—moving from ticket-based work to code-based self-service platforms.
Why It Matters
You are not just running a platform; you are building the reference implementation for the future of Salesforce. By solving the hardest engineering challenges internally—scaling CI/CD for Data Cloud, automating governance, and proving Tier 1a reliability—you pave the way for our customers to do the same. You will directly enable the Agentic Enterprise, ensuring that the data fueling our AI agents is trusted, timely, and resilient.
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.