Data 360 - Lead/ Principal Forward Deployed Engineer

About the role

What you'll do

• Build directly in customer sandboxes and production using Data 360 configuration, SQL, Apex, Flow, Python, REST Query API, Interaction SDK, Salesforce DX, CLI, Git, Data Kits, and deployment tools.
• Engineer batch and streaming ingestion, harmonization, identity resolution, and workload-optimized data views; tune latency and cost.
• Create representative tests and validate normal cases, edge cases, access boundaries, hierarchy behavior, failure conditions, and customer-scale volumes.
• Build and maintain RAG, vector databases, search indexes, and knowledge bases to ground Agentforce in accurate real-time data.
• Implement MCP and agent-to-agent communication protocols to govern and monitor collaboration among AI agents.
• Connect Agentforce to enterprise applications through robust, scalable, secure integration patterns.
• Manage secure integration, transformation, and governance of structured and unstructured data across Salesforce and external systems.
• Use message queues, event-driven architecture, and distributed systems to build resilient workflows; apply OAuth, SAML, and other secure authentication and authorization practices.
• Troubleshoot ingestion failures, mapping defects, schema drift, identity anomalies, query behavior, activation latency, API errors, permissions, and consumption issues.
• Work with architects, deployment strategists, Agentforce engineers, account teams, and customer technical teams.

Requirements

• Production implementation experience is required.
• A hands-on builder mindset is non-negotiable.
• Hands-on engineering experience includes SQL, Python, Apex, and building data ingestion pipelines.
• Able to build and ship implementations in customer environments from sandbox through production.
• Able to perform disciplined testing and produce a supportable handoff.

Technical scope

• Data 360 configuration and development, SQL, Apex, Flow, Python, Salesforce DX, Git, Data Kits, and deployment tooling.
• Data ingestion, harmonization, identity resolution, RAG, vector search, search indexes, knowledge bases, MCP, and agent-to-agent integration.
• REST Query API, Interaction SDK, message queues, event-driven architecture, distributed systems, OAuth, and SAML.
• Data strategies may include data mesh and data fabric.