Forward Deployed Engineer (FDE) - Data 360
$89k - $288k • New York - New York
Posted 1mo ago
Job Location
New York - New York
Tech Stack
Remote Work Policy
On-site
Categories
AI Engineer
About the job
Salesforce is seeking Forward Deployed Engineers (FDEs) to focus on Data 360, a real-time data engine that unifies data from various sources. In this hands-on role, you will configure, build, test, and deploy Data 360 within enterprise customer environments. You will translate approved solution designs into working implementations across data ingestion, harmonization, identity resolution, insights, activation, retrieval, security, and operational readiness. This role also involves deep data engineering for Agentforce, focusing on RAG, vector search, MCP, and agent-to-agent integration, ensuring agents are grounded in accurate, real-time enterprise data. You will collaborate with a delivery team including architects, deployment strategists, and customer technical teams, working on engagements that may range from proofs of concept to production rollouts and stabilization. This is a practical data and software engineering position requiring direct building within customer environments using SQL, Python, and Apex.
Responsibilities
- Build and configure solutions directly in customer sandboxes and production environments using Data 360 configuration, SQL, Apex, Flow, Python, REST Query API, Interaction SDK, Salesforce DX, CLI, Git, Data Kits, and deployment tooling, adhering to security and change management controls.
- Engineer the data layer by building ingestion, harmonization, and identity resolution pipelines for batch and streaming sources, optimizing data materialization for specific application, analytical, and agentic workloads.
- Create representative test data and validate expected outcomes across various scenarios, including happy paths, edge cases, access boundaries, hierarchy behavior, failure conditions, and customer-scale volumes.
- Design, build, and maintain the AI data integration layer, including RAG, vector databases, search indexes, and knowledge bases, to ground AI solutions in accurate, real-time enterprise data.
- Design and implement protocols for orchestrating agent communication, including Model Context Protocol (MCP) and agent-to-agent collaboration.
- Implement robust data integration patterns to connect AI solutions with enterprise applications and enable communication between AI agents.
- Apply data management expertise to ensure secure integration, transformation, and governance of structured and unstructured data across internal and external systems.
- Build resilient workflows and implement secure authentication and authorization protocols (OAuth, SAML) using knowledge of message queues, event-driven architecture, and distributed systems.
- Troubleshoot and resolve issues related to data ingestion, mapping, schema drift, identity anomalies, query behavior, activation latency, API errors, permissions, and consumption.
- Evaluate viable techniques within approved architectures, document tradeoffs, and recommend the most maintainable implementation to technical leads.
- Utilize AI tooling, including Data 360 APIs and MCP Servers, to automate the build process and accelerate customer time to value.
- Collaborate with customer technical teams and partners through pairing and code reviews, packaging reusable assets for support and reproduction.
- Support deployment, validation, production handoff, and early stabilization, ensuring clear ownership and documented limitations.
- Identify and report reproducible platform gaps and edge cases with evidence to Product and Support teams.
Requirements
- 5+ years of experience in software engineering or a related technical field.
- Production implementation experience is required.
- A builder mindset is non-negotiable.
- Experience writing SQL, Python, and Apex.
- Experience building ingestion pipelines.
- Experience debugging identity resolution.
- Experience shipping working implementations from sandbox through production.
- Experience with RAG, vector search, MCP, and agent-to-agent integration.
- Experience with data management, message queues, event-driven architecture, and distributed systems.
- Experience with secure authentication and authorization protocols (OAuth, SAML).
- Experience with Git and deployment tooling.
- Experience with data integration patterns.
- Experience with AI tooling, Data 360 APIs, and MCP Servers.