Architect, Data Platform — AgentExchange

Salesforce•San Francisco, CA

11d•Hybrid

About The Position

Salesforce is seeking a highly experienced Architect, Data Platform for its AgentExchange initiative. This role is crucial for owning the data and intelligence architecture for AgentExchange, a marketplace for partners to list, sell, and operate solutions across Agentforce, MuleSoft, Tableau, and Slack. The successful candidate will be the most senior technical voice for data on the platform, responsible for consolidating fragmented data pipelines into a single, trusted data platform. This involves defining data contracts, architecting an LLM- and agent-native layer to transform marketplace data into intelligent experiences, and engaging with executive stakeholders. The role requires a blend of architecture, strategy, and execution, including representing the data platform in cross-org architecture reviews and setting long-term technical direction.

Requirements

12+ years in software/data engineering, with multi-year ownership of an enterprise-scale data or ML platform.
Deep architecture experience in at least three of: lakehouse/warehouse design, streaming + batch pipelines, dimensional and event modeling, feature stores, model serving.
Experience with cloud-native data infrastructure: Snowflake, BigQuery, Redshift, or Databricks; AWS-based platforms.
LLM systems experience in production: RAG, embeddings and vector stores, prompt and context engineering, offline and online evaluation, cost and latency tuning, hallucination and safety controls.
Working knowledge of MCP or equivalent tool/agent protocols, and a clear point of view on exposing data to agents safely.
Data security and governance as a first-class skill: PII classification, multi-tenant isolation, fine-grained access control, GDPR/CCPA, lineage and audit, and security implications of LLM/agent access patterns.
Track record representing a technical domain in cross-org architecture forums and influencing direction across teams.
Executive communication skills: ability to defend architecture to a CTO and explain trade-offs to a PM.
A related technical degree.

Nice To Haves

Salesforce Data 360, Tableau Next, Slack, MuleSoft data integration.
Marketplace or e-commerce data experience: GMV, attrition, conversion funnels, search signal processing.
Experience with large-scale migrations (e.g., Heroku → cloud-native) with zero production disruption.
NPS and effort-score measurement architecture at scale.
Privacy-preserving ML (differential privacy, tokenization, synthetic data).
Agent evaluation frameworks and LLM observability (traces, eval datasets, regression suites).
Familiarity with Salesforce Platform features and best practices.

Responsibilities

Own end-to-end data architecture, including canonical data model, destination consolidation, telemetry taxonomy, and the 18-month roadmap for the AgentExchange data platform.
Define and govern pipelines and contracts, including streaming and batch ingestion, schema governance, data contracts enforced across all AgentExchange engineering teams, and pipeline reliability SLOs.
Develop self-service analytics capabilities, including Partner Console, GMV/attrition/install/search dashboards, and customer/partner effort scores, utilizing Data 360 and Tableau Next.
Architect and manage the ML platform, including feature store, training and serving infrastructure, evaluation, and monitoring.
Sponsor the Lead Scoring Model for AgentX partners and future predictive models (attrition, GMV forecasting, solution-pack recommendations).
Architect the LLM and agentic data layer, ensuring safe access to marketplace data through MCP servers, RAG over corpora, embeddings and vector store strategy, and evaluation harnesses.
Establish and enforce data security and governance standards, including PII handling, multi-tenant isolation, row- and column-level access, GDPR/CCPA compliance, audit, and privacy posture for LLM/agent surfaces.
Provide cross-org technical leadership, representing data in VAT and other architecture reviews, and aligning with Platform Services, Search & Personalization, and Partner Experience on shared standards.
Lead the data components of existing pipeline migrations with zero disruption to pipelines or partner analytics.
Mentor and elevate the technical capabilities of the Data Engineering & Analytics organization, review designs, and grow senior individual contributors.