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Architect Engineer, Data Platform Services

4 Locations💼 Full-time🗓 2026-06-29 → 2026-07-31

Core

Define and lead the technical strategy for Salesforce's Data Platform Services (DPS), building the data foundation that powers AI transformation and Agentforce products.

Role type

Distinguished Engineer (Senior IC)

Builds

Trusted, governed, and semantically rich data platforms consumed by internal business units and external customers.

Domain

Cloud-native data platform engineering and AI readiness

Deliverable

production ML models | infrastructure

Required skills

distributed systems design, cloud-native platform architecture, data platform architecture (lakehouse, governance, contracts), AI/ML workload enablement, agentic development patterns, engineering standards setting, infrastructure-as-code, containerization, CI/CD

Preferred skills

data mesh/federated data organization, semantic layer design, knowledge graphs, enterprise data governance, Salesforce platform capabilities (Data Cloud, Agentforce, MuleSoft)

Technologies

Kubernetes, Docker, AWS, GCP, Azure

Responsibilities

Define AI readiness strategy for data governance and enrichment; own technical vision for Data-as-a-Product platform; set engineering standards for design, build, and operation; lead reliability and scalability strategy; serve as authoritative technical voice in cross-team decisions; coach senior engineers and PMTS.

Seniority

Distinguished Engineer, hands-on IC with strategic scope

Rewrite
## Responsibilities - 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 DPS's technical vision to CDO leadership and peer platform teams. - Customer Zero. Participate in Salesforce's 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. ## Requirements - 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. ## Nice to Have - None specified. ## Benefits - Competitive salary and benefits package. - Opportunities for career growth and leadership. - Collaborative and innovative work environment. - Access to cutting-edge technology and AI initiatives. - Global impact through shaping the future of Salesforce's data platform.
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