Data Platform Architect (AgentExchange)

• AgentExchange runs on telemetry, partner analytics, and ML signals — and today they live in fragmented pipelines and destinations
• We need an architect to consolidate them into one trusted data platform, define the contracts every team builds to, and architect the LLM- and agent-native layer that turns marketplace data into intelligent experiences for partners, customers, and Salesforce leadership
• This is a role at the intersection of architecture, strategy, and execution
• You will engage with executive stakeholders, represent the data platform in cross-org architecture reviews (VAT), and set the long-term technical direction every data engineer and ML practitioner on the team works toward
• End-to-end data architecture. Canonical data model, destination consolidation, telemetry taxonomy, and the 18-month roadmap for the AgentExchange data platform
• Pipelines and contracts. Streaming and batch ingestion, schema governance, data contracts enforced across every AgentExchange engineering team, and pipeline reliability SLOs
• Self-service analytics. Partner Console, GMV / attrition / install / search dashboards, customer and partner effort scores — built on Data 360 and Tableau Next
• ML platform. Feature store, training and serving infrastructure, evaluation, and monitoring. Sponsor the Lead Scoring Model for AgentX partners and the next wave (attrition, GMV forecasting, solution-pack recommendations)
• LLM and agentic data layer. Architect how agents access marketplace data safely — including MCP servers that expose curated data tools to internal and partner-facing agents, RAG over partner / listing / telemetry corpora, embeddings and vector store strategy, and evaluation harnesses for LLM-driven insights
• Data security and governance. Set the bar for PII handling, multi-tenant isolation, row- and column-level access, GDPR / CCPA, audit, and the privacy posture of any LLM or agent surface that touches partner or customer data
• Cross-org technical leadership. Represent data in VAT and cross-org architecture reviews; align Platform Services, Search & Personalization, and Partner Experience on shared standards
• Migration leadership. Drive the data components of existing pipeline migration with zero disruption to pipelines or partner analytics
• Mentorship. Be the top technical sponsor for the Data Engineering & Analytics organization — raise the bar on craft, review designs, and grow the next generation of senior ICs
• What Success Looks Like (First 12–15 Months):
• One unified analytics destination replaces today’s fragmented stack; every AgentExchange team publishes against a shared contract
• Partner-facing dashboards refresh on a documented SLO, and instrumentation completeness is measurable and enforced
• An MCP-based agentic data layer is in production, with clear guardrails for what agents can read, summarize, and act on
• The Lead Scoring model and at least one new predictive surface (attrition or GMV forecasting) are in production with offline and online evaluation

Benefits

• Medical Care
• Life Insurance
• Retirement Savings
• Employee Assistance Programs
• With 9 standard holidays and four floating holidays, you get a total 13 paid days off each year- Track record representing a technical domain in cross-org architecture forums and influencing direction across teams you don’t manage
• Large-scale migrations (Heroku → cloud-native) with zero production disruption
• Marketplace or e-commerce data: GMV, attrition, conversion funnels, search signal processing
• Executive communication: can defend an architecture to a CTO and explain trade-offs to a PM in the same hour
• Privacy-preserving ML (differential privacy, tokenization, synthetic data)
• Working knowledge of MCP or equivalent tool / agent protocols, and a clear point of view on exposing data to agents safely
• Agent evaluation frameworks and LLM observability (traces, eval datasets, regression suites)
• Salesforce Data 360, Tableau Next, Slack, MuleSoft data integration
• Familiarity with Salesforce Platform features and best practices
• Data security and governance as a first-class skill: PII classification, multi-tenant isolation, fine-grained access control, GDPR / CCPA, lineage and audit, and the security implications of LLM / agent access patterns
• Preferred
• Deep architecture experience in at least three of: lakehouse / warehouse design, streaming + batch pipelines, dimensional and event modeling, feature stores, model serving
• 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
• NPS and effort-score measurement architecture at scale
• Cloud-native data infrastructure: Snowflake, BigQuery, Redshift, or Databricks; AWS-based platforms
• A related technical degree required
• 12+ years in software / data engineering, including multi-year ownership of an enterprise-scale data or ML platform
• In office expectations are 10 days/a quarter to support customers and/or collaborate with their teams