Forward Deployed Engineer (FDE) - Data 360

Salesforce•Chicago, NY

About The Position

Salesforce is hiring Forward Deployed Engineers focused on Data 360, Salesforce’s real-time data engine that unifies data from any source. As a Forward Deployed Engineer, you will be a hands-on Technical Builder who configures, builds, tests, and deploys Data 360 directly inside enterprise customer environments. You will turn approved solution designs and acceptance criteria into working implementations across ingestion, harmonization, identity, insights, activation, retrieval, security, and operational readiness. Data 360 is also the data foundation for Agentforce - and grounding agents in accurate, real-time enterprise data is deep data engineering. You'll go further into RAG, vector search, MCP, and agent-to-agent integration than most data roles ever touch, because the agents are only as good as the layer you build. You'll work within a delivery team that includes architects, deployment strategists, Agentforce FDEs, account teams, and customer technical teams. Engagements may span a focused proof, production rollout, and stabilization. The common thread is real customer data, disciplined testing, and a supportable handoff. This is a hands-on data and software engineering role. You will build in real enterprise customer environments - writing SQL, Python, and Apex; building ingestion pipelines; debugging identity resolution; and shipping working implementations from sandbox through production. If you're looking for a purely declarative configuration role or a pure advisory role, this isn't it.

Requirements

5+ years of experience in software engineering, data engineering, or technical implementation, including a production data solution you can explain in detail.
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 MDM platform.
Proficient in SQL and can build, debug, and optimize multi-table queries, joins, filters, transformations, and validation checks.
Fluent in at least one implementation language used in enterprise delivery - Apex, Java, Python, or JavaScript/TypeScript - and are willing to learn the others as needed.
Understand data modeling, APIs, integration patterns, batch and streaming processing, and the practical differences between copied, federated, and Zero Copy data access.
Have built retrieval systems for AI applications - vector databases, search indexes, embedding pipelines, or knowledge bases - and can explain the design decisions behind chunking, indexing, and relevance.
Have implemented secure service-to-service integration patterns (OAuth, SAML, event-driven architectures) and are conversant in emerging agent protocols like MCP and agent-to-agent communication.
Can implement secure data access and rigorously test it, including permissions, entitlements, consent, user context, restricted populations, and negative test cases.
Diagnose technical problems methodically, use logs and query evidence, isolate product behavior from configuration errors, and document a reproducible result.
Can implement against a documented architecture, explain what you built to customer technical teams, and recognize when a question requires an architect or security owner.
Have delivered alongside customers, partners, professional services, or internal implementation teams in deadline-driven environments.
Excited to work directly with customer administrators, developers, data engineers, and architects.

Nice To Haves

Relevant certifications in Salesforce Agentforce, Data 360, and the Salesforce Platform.

Responsibilities

Build in customer environments. 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 as required - following security, change-management, and release controls.
Engineer the data layer. Build ingestion, harmonization, and identity resolution across batch and streaming sources. Materialize views of data optimized for specific application, analytical, and agentic workloads, tuning latency and cost tradeoffs to meet use-case requirements. Work with customer data and AI teams to ensure implementations align with their existing strategies (e.g., data mesh, data fabric).
Prove it works. Create representative test data and validate expected outcomes across happy paths, edge cases, access boundaries, hierarchy behavior, failure conditions, and customer-scale volumes.
Ground the agents. Design, build, and maintain the AI data integration layer - RAG (Retrieval-Augmented Generation), vector databases, search indexes, and knowledge bases - that grounds Agentforce solutions in accurate, real-time enterprise data.
Orchestrate agent communication. Design and implement the protocols - including Model Context Protocol (MCP) and agent-to-agent communication - that govern, monitor, and ensure efficient collaboration among specialized AI agents.
Connect the ecosystem. Implement robust, scalable, and secure data integration patterns that connect Agentforce to a wide range of enterprise applications and enable communication between AI agents.
Govern the data. Apply deep data management expertise to ensure the secure integration, transformation, and governance of structured and unstructured data across the Salesforce ecosystem (CRM, Data Cloud) and external systems.
Build for scale and security. Apply knowledge of message queues, event-driven architecture, and distributed systems to build resilient workflows and implement secure authentication and authorization protocols (OAuth, SAML) so that all agent actions are secure and comply with enterprise security policies.
Debug the hard problems. Troubleshoot ingestion failures, mapping defects, schema drift, identity anomalies, query behavior, activation latency, API errors, permissions, and consumption issues - methodically, with logs and query evidence, isolating product behavior from configuration error.
Own implementation decisions. Compare viable techniques within the approved architecture, document measured tradeoffs, and recommend the most maintainable implementation to the responsible architect or technical lead.
Accelerate with AI. Use AI tooling - including Data 360 APIs and MCP Servers - to automate the build process and compress customers' time to value.
Co-build and hand off. Work alongside customer technical teams and partners through pairing, code reviews, and configuration reviews. Package reusable metadata, scripts, queries, tests, and runbooks so the solution can be reproduced and supported. Support deployment, validation, production handoff, and early stabilization, leaving clear ownership and known limitations.
Feed the roadmap. Surface reproducible platform gaps and edge cases - with live evidence, impact, and expected behavior - directly to Product and Support.