Architect Engineer, Data Platform Services

About the role

Data Platform Services (DPS) is engineering the data foundation that powers Salesforce's 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 Salesforce's largest internal business units every day.

This is DPS's 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 Salesforce's broader AI and data strategy.

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 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).

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 vendor's 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.