Software Engineering PMTS - Data Platform

SalesforceSan Francisco, CA
Hybrid

About The Position

Salesforce is seeking a highly experienced technical leader to own the data and intelligence architecture for AgentExchange, a marketplace for partners to list, sell, and operate various Salesforce solutions. This role is crucial for consolidating fragmented data pipelines and destinations into a single, trusted data platform. The position involves defining data contracts, architecting an LLM- and agent-native layer to transform marketplace data into intelligent experiences, and engaging with executive stakeholders. It sits at the intersection of architecture, strategy, and execution, requiring representation in cross-organizational architecture reviews and setting long-term technical direction for the data engineering and ML teams.

Requirements

  • 10+ 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.
  • Production LLM systems experience: 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 the 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: GMV, attrition, conversion funnels, search signal processing.
  • Large-scale migrations (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

  • End-to-end data architecture, including canonical data model, destination consolidation, telemetry taxonomy, and the 18-month roadmap for the AgentExchange data platform.
  • Define 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, built on Data 360 and Tableau Next.
  • Manage the ML platform, including feature store, training and serving infrastructure, evaluation, and monitoring. Sponsor the Lead Scoring Model and other predictive models (attrition, GMV forecasting, solution-pack recommendations).
  • Architect the LLM and agentic data layer, defining how agents access marketplace data safely, including MCP servers, RAG over corpora, embeddings and vector store strategy, and evaluation harnesses.
  • Establish data security and governance standards, including PII handling, multi-tenant isolation, access control, GDPR/CCPA compliance, audit, and privacy posture for LLM/agent surfaces.
  • Provide cross-organizational technical leadership, representing data in architecture reviews and aligning with other platform teams on shared standards.
  • Lead the data components of existing pipeline migration with zero disruption.
  • Serve as the top technical sponsor for the Data Engineering & Analytics organization, raising the bar on craft, reviewing designs, and mentoring senior ICs.

Benefits

  • Time off programs
  • Medical insurance
  • Dental insurance
  • Vision insurance
  • Mental health support
  • Paid parental leave
  • Life insurance
  • Disability insurance
  • 401(k)
  • Employee stock purchasing program
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