6 days ago

Data360 FDE

Salesforce

Amsterdam

6 days ago

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Data360 FDE

Salesforce is hiring a Forward Deployed Engineer focused on Data 360, a real-time data engine. This hands-on role involves configuring, building, testing, and deploying Data 360 in enterprise customer environments, including data engineering, AI grounding, and agent orchestration. Requires 5+ years of experience in software/data engineering and proficiency in SQL and at least one implementation language.

AI-enabledRemoteFull-timeMid LevelData 360Data Cloud

Data360 FDE

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AI-enabledRemoteFull-timeMid LevelData 360

Salary

Not specified

Work Location

Amsterdam, North Holland, Netherlands, NL

Work Model

Remote

Experience Required

5 years

Employment Type

Full-time

Experience Level

5+ years of experience in software engineering, data engineering, or technical implementation

Core Qualifications

Technical (Must-have)

Data 360Data CloudSQLApexPythonJavaJavaScriptTypeScriptREST APIsSalesforce DXCLIGitData KitsRAGVector DatabasesMCPOAuthSAMLSnowflakeDatabricksBigQueryAmazon RedshiftApache KafkaEvent-Driven ArchitectureData ModelingETLData IntegrationCI/CD

Soft Skills

Problem SolvingCommunicationCollaborationMethodicalCuriosityAdaptability

Key Responsibilities

•Build in customer environments using Data 360 configuration, SQL, Apex, Flow, Python, REST Query API, Interaction SDK, Salesforce DX, CLI, Git, Data Kits, and deployment tooling.
•Engineer the data layer: build ingestion, harmonization, and identity resolution across batch and streaming sources.
•Prove it works: create representative test data and validate expected outcomes across various scenarios.
•Ground the agents: design, build, and maintain the AI data integration layer (RAG, vector databases, search indexes, knowledge bases).
•Orchestrate agent communication: implement protocols including MCP and agent-to-agent communication.
•Connect the ecosystem: implement data integration patterns to connect Agentforce to enterprise applications.
•Govern the data: ensure secure integration, transformation, and governance of structured and unstructured data.
•Build for scale and security: apply knowledge of message queues, event-driven architecture, and distributed systems.
•Debug the hard problems: troubleshoot ingestion failures, mapping defects, schema drift, identity anomalies, etc.
•Own implementation decisions: compare viable techniques and recommend the most maintainable implementation.
•Accelerate with AI: use AI tooling including Data 360 APIs and MCP Servers to automate the build process.
•Co-build and hand off: work alongside customer technical teams and partners, package reusable metadata, scripts, queries, tests, and runbooks.
•Feed the roadmap: surface reproducible platform gaps and edge cases to Product and Support.

Data EngineeringSalesforceData 360AgentforceAIRAGMCPCloudFull-timeRemote

Core Qualifications

Technical (Must-have)

Soft Skills

Problem SolvingCommunicationCollaborationMethodicalCuriosityAdaptability

Key Responsibilities

•Build in customer environments using Data 360 configuration, SQL, Apex, Flow, Python, REST Query API, Interaction SDK, Salesforce DX, CLI, Git, Data Kits, and deployment tooling.

•Engineer the data layer: build ingestion, harmonization, and identity resolution across batch and streaming sources.

•Prove it works: create representative test data and validate expected outcomes across various scenarios.

•Ground the agents: design, build, and maintain the AI data integration layer (RAG, vector databases, search indexes, knowledge bases).

•Orchestrate agent communication: implement protocols including MCP and agent-to-agent communication.

•Connect the ecosystem: implement data integration patterns to connect Agentforce to enterprise applications.

•Govern the data: ensure secure integration, transformation, and governance of structured and unstructured data.

•Build for scale and security: apply knowledge of message queues, event-driven architecture, and distributed systems.

•Debug the hard problems: troubleshoot ingestion failures, mapping defects, schema drift, identity anomalies, etc.

•Own implementation decisions: compare viable techniques and recommend the most maintainable implementation.

•Accelerate with AI: use AI tooling including Data 360 APIs and MCP Servers to automate the build process.

•Co-build and hand off: work alongside customer technical teams and partners, package reusable metadata, scripts, queries, tests, and runbooks.

•Feed the roadmap: surface reproducible platform gaps and edge cases to Product and Support.