Architect Engineer, Data Platform Services

Salesforce Salesforce · Enterprise · San Francisco, New York - New York, Washington - Seattle, Washington - Bellevue, CA

Salesforce is seeking a Distinguished Engineer to lead the technical strategy for their Data Platform Services (DPS). This role focuses on building and operating the data foundation that powers Salesforce's AI transformation, ensuring trusted, governed, and semantically rich data is available for AI agents and LLM pipelines. The engineer will define standards for data-as-a-product, platform reliability, and scale, and influence the roadmap for AI-native consumption patterns.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.

Skills

Required

  • software or platform engineering
  • technical strategy definition and execution
  • distributed systems design
  • cloud-native platform architecture
  • reliability and scalability patterns
  • data platform architecture
  • lakehouse design
  • data catalog and governance
  • data contract frameworks
  • metadata management
  • enabling AI/ML workloads
  • agent integration patterns
  • agentic development patterns
  • full product development lifecycle

What the JD emphasized

  • AI-native workloads
  • agent-ready assets
  • agent integration patterns
  • agent development lifecycle

Other signals

  • AI-native workloads
  • agent-ready context delivery
  • AI transformation
  • Agentforce