Data Platform Services (DPS) is engineering the data foundation that powers Salesforces AI transformation. We build and operate the platforms that make trusted, governed, and semantically rich data available at the speed and scale that Agentforce and next-generation AI products demand. Our work spans the full data lifecycle — from ingestion and lakehouse architecture to data-as-a-product (DaaP), quality, observability, and agent-ready context delivery — and our platform is consumed by Salesforces largest internal business units every day.
We are looking for a Distinguished Engineer to define and lead the technical strategy for this platform. This is DPSs most senior individual contributor role. You will shape how the platform evolves to support AI-native consumption patterns, set the standards that engineering teams across DPS build to, and be the technical voice that connects platform capabilities to Salesforces broader AI and data strategy.
You will report directly to the VP of Data Platform Services and will partner closely with Architects and Engineering leaders across the organization, with product leadership, and with Salesforces Product and Engineering teams as part of Salesforces Customer Zero programs — where DPS both validates and influences the data platform capabilities that ship to Salesforces external customers.
What Youll Do
• AI Readiness Strategy. Define how the DPS platform evolves to support AI-native workloads — including how data is governed, enriched, and surfaced for reliable consumption by AI agents and LLM pipelines.
• Data-as-a-Product Platform. Own the technical vision for transforming raw datasets into high-fidelity, discoverable, and agent-ready assets. This means defining the global standards for automated data contracts, versioned schemas, and enforceable SLAs. Your goal is to build the self-service infrastructure that allows domain teams to ship data with the same rigor, quality, and observability as a production microservice — ensuring that every data product in the DPS catalog is trusted by default.
• Engineering Standards & Architecture. Set the standards governing how DPS teams design, build, and operate platform services — API design, data contracts, reliability, observability, and secure-by-default service ownership — and drive adoption across the organization.
• Platform Reliability & Scale. Lead the reliability and scalability strategy for DPS platform services, establishing the architectural principles, SLO frameworks, and operational standards that govern platform performance at enterprise scale.
• Technical Leadership. Serve as the authoritative technical voice across design reviews, cross-team architecture decisions, and long-range planning — surfacing tradeoffs clearly, resolving cross-organizational dependencies, and partnering with Engineering Directors to connect technical investment to business outcomes.
• Engineering Multiplier. Invest in coaching PMTS and senior engineers across DPS, raise the technical quality of the organization through shared design patterns and written proposals, and represent DPSs technical vision to CDO leadership and peer platform teams.
• Customer Zero. Participate in Salesforces Customer Zero programs — providing structured technical feedback on Data Cloud, Agentforce, and platform capabilities from the perspective of an enterprise-scale internal operator, and collaborating with Salesforce Product teams to influence the roadmap.
What Were Looking For
Required
• Significant experience in software or platform engineering — typically 12 to 15+ years — with demonstrated impact at the scope of a senior or principal IC: owning technical direction across multiple teams, shaping multi-year roadmaps, and influencing engineering outcomes at an organizational level.
• Demonstrated ability to define and drive a multi-year technical strategy for a platform organization — not just contribute to it, but own it and make it legible and actionable for engineering and business audiences.
• Deep expertise in distributed systems design, cloud-native platform architecture, and the reliability and scalability patterns that govern enterprise-scale platform services.
• Strong understanding of data platform architecture — including lakehouse design, data catalog and governance, data contract frameworks, and metadata management — from a platform engineering perspective.
• Experience shaping how data platforms enable AI/ML workloads — including how data is structured, governed, and served for agentic or LLM-based consumption — and hands-on familiarity with agent integration patterns such as MCP or equivalent protocols for connecting data systems to AI runtimes.
• Fluency in agentic development patterns — including the agent development lifecycle from prompt and tool design through evaluation, deployment, and observability — and a working understanding of how data platform decisions shape what agents can reliably do. Paired with a strong grasp of the full product development lifecycle, from early discovery through production operations, with the ability to hold both perspectives simultaneously
• Track record of setting engineering standards — including data quality frameworks, automated data contracts, and SLA enforcement — and building the technical culture and tooling that makes those standards easy to adopt across teams.
• Working knowledge of infrastructure-as-code, containerization (Kubernetes, Docker), and CI/CD patterns in a cloud environment (AWS, GCP, or Azure).
• Excellent written and verbal communication skills, including the ability to make complex architectural tradeoffs legible to both engineering and executive audiences.
• Ability to lead through influence — comfortable setting direction without authority, building consensus, and holding teams to high standards through dialogue.
Preferred
• Experience operating within a data mesh or federated data organization, where platform services and domain-owned data products coexist.
• Experience with semantic layer design and knowledge graph-based approaches to enriching data for AI and analytics consumption.
• Background in enterprise data governance — including attribute-based access control, data classification, and regulatory compliance frameworks at scale.
• Experience in a Customer Zero or product co-development capacity — working directly with a vendors product team to influence the roadmap based on real operational experience.
• Familiarity with Salesforce platform capabilities — Data Cloud, Agentforce, MuleSoft, Tableau Next — from an operator or integration perspective.
• BS/MS in Computer Science, Software Engineering, or a related technical discipline, or equivalent work experience.
The Right Fit
This role is for someone who thinks at the intersection of platform engineering, AI readiness, and technical strategy — and who has the credibility and communication skills to move organizations toward a shared technical vision. You will be most effective here if you:
• Can articulate a three-year platform strategy and still care deeply about whether the API Design is right.
• Are as comfortable writing a proposal for CDO leadership as you are reviewing a service design with a senior engineer.
• See data governance, semantic enrichment, and observability as foundational AI infrastructure — not compliance overhead.
• Have strong opinions about what makes a platform trustworthy and scalable, and can back those opinions with data and engineering rationale.
• Operate with extreme ownership — you see gaps and close them, whether that means writing the spec, reviewing the design, or escalating to the right person.