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Salesforce

Builds cloud software for customer relationships

Forward Deployed Engineer - Data

Backend EngineeringConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • At least 5 years of experience in software engineering, data engineering, or technical implementation, including experience with a production data solution.
  • A bachelor's degree in Computer Science or equivalent practical experience.
  • Hands-on experience implementing Salesforce Data 360 (Data Cloud) or a comparable enterprise data platform across ingestion, modeling, identity, insight, activation, and operations.
  • Hands-on experience building data pipelines and integrations using at least one enterprise data platform or technology, such as Snowflake, Databricks, BigQuery, Redshift, Kafka, cloud object storage, or an enterprise master data management platform.
  • Proficiency in SQL, including building, debugging, and optimizing multi-table queries, joins, filters, transformations, and validation checks.
  • Fluency in at least one implementation language used in enterprise delivery: Apex, Java, Python, or JavaScript/TypeScript, and willingness to learn others as needed.
  • Understanding of data modeling, APIs, integration patterns, batch and streaming processing, and copied, federated, and Zero Copy data access.
  • Experience building retrieval systems for AI applications, such as vector databases, search indexes, embedding pipelines, or knowledge bases, and explaining decisions involving chunking, indexing, and relevance.
  • Experience implementing secure service-to-service integration patterns, including OAuth, SAML, and event-driven architectures, and familiarity with MCP and agent-to-agent communication.
  • Ability to implement secure data access and test permissions, entitlements, consent, user context, restricted populations, and negative test cases.
  • Ability to diagnose technical problems using logs and query evidence, isolate product behavior from configuration errors, and document reproducible results.
  • Ability to implement against a documented architecture, explain implementations to customer technical teams, and recognize when to escalate a question to an architect or security owner.
  • Experience delivering alongside customers, partners, professional services, or internal implementation teams in deadline-driven environments.
  • Relevant certifications in Salesforce Agentforce, Data 360, and the Salesforce Platform.

Responsibilities

  • Configure and develop directly in customer sandboxes and production using Data 360 configuration, SQL, Apex, Flow, Python, the REST Query API, the Interaction SDK, Salesforce DX, CLI, Git, Data Kits, and deployment tooling, following security, change-management, and release controls.
  • Build ingestion, harmonization, and identity resolution across batch and streaming sources; materialize data views optimized for application, analytical, and agentic workloads; tune latency and cost tradeoffs; and align implementations with customers’ data mesh or data fabric strategies.
  • Create representative test data and validate outcomes across happy paths, edge cases, access boundaries, hierarchy behavior, failure conditions, and customer-scale volumes.
  • Design, build, and maintain the AI data integration layer, including retrieval-augmented generation, vector databases, search indexes, and knowledge bases, to ground Agentforce solutions in accurate, real-time enterprise data.
  • Design and implement protocols, including Model Context Protocol and agent-to-agent communication, to govern and monitor collaboration among specialized AI agents and ensure its efficiency.
  • Implement scalable and secure data integration patterns connecting Agentforce to enterprise applications and enabling communication between AI agents.
  • Ensure secure integration, transformation, and governance of structured and unstructured data across the Salesforce ecosystem and external systems.
  • Use message queues, event-driven architecture, and distributed systems to build resilient workflows; implement secure authentication and authorization protocols, including OAuth and SAML, to comply with enterprise security policies.
  • Troubleshoot ingestion failures, mapping defects, schema drift, identity anomalies, query behavior, activation latency, API errors, permissions, and consumption issues using logs and query evidence to distinguish product behavior from configuration errors.
  • Compare viable techniques within the approved architecture, document measured tradeoffs, and recommend maintainable implementations to the responsible architect or technical lead.
  • Use AI tooling, including Data 360 APIs and MCP Servers, to automate the build process and reduce customer time to value.
  • Work alongside customer technical teams and partners through pairing, code reviews, and configuration reviews; package reusable metadata, scripts, queries, tests, and runbooks; and support deployment, validation, production handoff, and early stabilization while documenting ownership and known limitations.
  • Report reproducible platform gaps and edge cases, with evidence, impact, and expected behavior, to Product and Support.

About the Company

Salesforce

Builds cloud software for customer relationships

Salesforce sells cloud software that helps businesses manage sales leads, customer service, marketing, and online commerce from shared customer records. Its flagship Sales Cloud tracks accounts and deals, while Service Cloud organizes support cases and Agentforce builds AI assistants that act on company data. Teams access these tools through a browser and connect outside apps and data sources, with Slack for work messages and Tableau for visual analysis. Salesforce earns most of its revenue from software subscriptions and support, with professional services for implementation and training. Marc Benioff co-founded Salesforce in 1999, and its headquarters are in San Francisco, California.

Company size
10,001+
Company stage
IPO
Headquarters
San Francisco, CA
Founded
1999

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