Salesforce

2 months ago

Architect, Data Platform — AgentExchange

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 9 minutes agoStatus: Live

Job Summary

Lead the data architecture for AgentExchange, consolidating fragmented telemetry and analytics pipelines into a unified data platform. Define data contracts, govern schemas, and design the LLM- and agent-native layer for secure, scalable access to marketplace data. Drive cross-org architectural direction, mentor engineers, and oversee migration efforts with executive stakeholders.

Location: San Francisco, Bellevue

Workplace: Hybrid

Employment Type: Full time

Job Function: Solutions Engineering & Sales Engineering

Key Responsibilities

•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 — MCP servers, RAG over partner / listing / telemetry corpora, embeddings and vector store strategy, and evaluation harnesses for LLM-driven insights.

Pay and Benefits

Perks:Health InsuranceDentalVision401kEquity

Key Requirements

•12+ years in software / data engineering, including 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.
•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.

Experience:12+ yearsCRMAIData platformCloud

Education:Bachelor's

Skills:LeadershipCommunicationStakeholder managementMentorshipProblem-solving

Languages:English

Tech Stack:SnowflakeBigQueryRedshiftDatabricksAWSLLMRAGEmbeddingsVector storeMCPTableauData 360

Company Brief

Salesforce

Provides a leading cloud-based customer relationship management (CRM) platform with sales, service, marketing, analytics, and integration tools that empower businesses to manage customer relationships and digital transformation at scale.

Industry: SaaS

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: San Francisco, United States

Founded: 1999

Glassdoor: 4.1

Website LinkedIn Glassdoor

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Salesforce

2 months ago

Architect, Data Platform — AgentExchange

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 9 minutes agoStatus: Live

Job Summary

Location: San Francisco, Bellevue

Workplace: Hybrid

Employment Type: Full time

Job Function: Solutions Engineering & Sales Engineering

Key Responsibilities

•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 — MCP servers, RAG over partner / listing / telemetry corpora, embeddings and vector store strategy, and evaluation harnesses for LLM-driven insights.

Pay and Benefits

Perks:Health InsuranceDentalVision401kEquity

Key Requirements

•12+ years in software / data engineering, including 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.
•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.

Experience:12+ yearsCRMAIData platformCloud

Education:Bachelor's

Skills:LeadershipCommunicationStakeholder managementMentorshipProblem-solving

Languages:English

Tech Stack:SnowflakeBigQueryRedshiftDatabricksAWSLLMRAGEmbeddingsVector storeMCPTableauData 360

Company Brief

Salesforce

Industry: SaaS

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: San Francisco, United States

Founded: 1999

Glassdoor: 4.1

Website LinkedIn Glassdoor