Software Engineering PMTS (Data Platform)

• Alesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we’re looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce’s core values at the heart of it all
• Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce
• Own the data and intelligence architecture for AgentExchange — the marketplace where partners list, sell, and operate Agentforce, MuleSoft, Tableau, and Slack solutions. You are the most senior technical voice for data on the platform
• The AgentExchange Data Services team owns the data infrastructure, telemetry pipelines, marketplace measurement framework, partner analytics platform, and the ML-powered signals that give partners, customers, and Salesforce leadership real-time visibility into how the ecosystem is performing
• The team owns product telemetry, self-service metrics, GMV and attrition models, NPS and effort-score measurement, and the Lead Scoring Model for AgentX partners
• 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- Cloud-native data infrastructure: Snowflake, BigQuery, Redshift, or Databricks; AWS-based platforms
• Track record representing a technical domain in cross-org architecture forums and influencing direction across teams you don’t manage
• 10+ years in software / data engineering, including multi-year ownership of an enterprise-scale data or ML platform
• Executive communication: can defend an architecture to a CTO and explain trade-offs to a PM in the same hour
• Deep architecture experience in at least three of: lakehouse / warehouse design, streaming + batch pipelines, dimensional and event modeling, feature stores, model serving
• 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
• Working knowledge of MCP or equivalent tool / agent protocols, and a clear point of view on exposing data to agents safely
• 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
• A related technical degree required
• Salesforce Data 360, Tableau Next, Slack, MuleSoft data integration
• Marketplace or e-commerce data: GMV, attrition, conversion funnels, search signal processing
• NPS and effort-score measurement architecture at scale
• Large-scale migrations (Heroku → cloud-native) with zero production disruption
• 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