Description
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.
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.
- 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, 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 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 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.
- Debug the hard problems. Troubleshoot ingestion failures, mapping defects, schema drift, identity anomalies, query behavior, activation latency, API errors, permissions, and consumption issues.
- Own implementation decisions. Compare viable techniques within the approved architecture, document measured tradeoffs, and recommend the most maintainable implementation.
- Accelerate with AI. Use AI tooling 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.
- Feed the roadmap. Surface reproducible platform gaps and edge cases directly to Product and Support.
Requirements:
- 8+ 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 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.
- Proficient in SQL and can build, debug, and optimize multi-table queries.
- Fluent in at least one implementation language used in enterprise delivery.
- Understand data modeling, APIs, integration patterns, batch and streaming processing.
- Built retrieval systems for AI applications.
- Implemented secure service-to-service integration patterns.
- Diagnose technical problems methodically.
- Can implement against a documented architecture.
- 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.
- Relevant certifications in Salesforce Agentforce, Data 360, and the Salesforce Platform