Data 360 - Lead/ Principal Forward Deployed Engineer

About The Role
Production implementation experience required. Builder mindset non-negotiable.
The company is hiring Forward Deployed Engineers focused on Data 360, the company'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 - grounding agents in accurate, real-time enterprise data requires deep data engineering. You'll engage with technologies such as RAG, vector search, MCP, and agent-to-agent integration, working beyond what most data roles typically cover, as the agents rely heavily on the robust layers you construct.
You'll collaborate within a delivery team that includes architects, deployment strategists, Agentforce FDEs, account teams, and customer technical teams. Engagements may range from focused proofs, production rollouts, to stabilization. The common thread across these projects is the use of real customer data, disciplined testing, and a structured support handoff.
This is a hands-on data and software engineering role. You will build in real enterprise customer environments - writing SQL, Python, and Apex; constructing ingestion pipelines; debugging identity resolution; and delivering working implementations from sandbox through to production. This is not a role focused strictly on declarative configuration or advisory tasks.

What You'll Do

• 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, the company DX, CLI, Git, Data Kits, and deployment tooling as required, while adhering to security, change-management, and release controls.
• Engineer the data layer. Construct ingestion, harmonization, and identity resolution across batch and streaming sources. Materialize views of data optimized for specific applications, analytical, and agentic workloads by tuning latency and cost trade-offs. Collaborate with customer data and AI teams to ensure implementations are consistent with their existing strategies (e.g., data mesh, data fabric).
• Prove it works. Generate representative test data and validate expected outcomes across successful scenarios, edge cases, access boundaries, hierarchy behavior, failure conditions, and significant 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 - ensuring Agentforce solutions are built on accurate, real-time enterprise data.
• Orchestrate agent communication. Design and implement protocols, including Model Context Protocol (MCP) and inter-agent communications, to govern and facilitate efficient collaboration among specialized AI agents.
• Connect the ecosystem. Establish robust, scalable, and secure data integration patterns that link Agentforce to a variety of enterprise applications and promote communication between AI agents.
• Govern the data. Utilize expertise in data management to ensure secure integration, transformation, and governance of structured and unstructured data across the company ecosystem (CRM, Data Cloud) and external systems.
• Build for scale and security. Leverage knowledge of message queues, event-driven architecture, and distributed systems to construct resilient workflows and apply secure authentication and authorization protocols (OAuth, SAML) ensuring all agent actions are secure and enterprise-compliant.
• Debug challenging issues. Methodically troubleshoot ingestion failures, mapping defects, schema drift, identity anomalies, query behaviors, activation delays, API errors, permissions, and consumption concerns, utilizing logs and query evidence to isolate product behavior from configuration errors.
• Own implementation decisions. Evaluate feasible techniques within the approved architecture, document measured trade-offs, and advocate for the most maintainable implementation to the responsible architect or technical lead.
• Accelerate with AI. Employ AI tooling, including Data 360 APIs and MCP Servers, to automate the build process and reduce customers' time to value.
• Co-build and hand off. Collaborate with customer technical teams and partners through code pairing, reviews, and configuration assessments. Package reusable metadata and scripts so solutions can be supported and replicated effectively, managing the deployment, validation, production handoff, and stabilization process while ensuring clear ownership and known limitations.
• Feed the roadmap. Highlight reproducible platform gaps and edge cases, providing live evidence, impact assessments, and expected behaviors directly to Product and Support teams.

You're Our Data 360 Forward Deployed Engineer If...

• You have 8+ years of experience in software engineering, data engineering, or technical implementation, including a production data solution you can thoroughly explain.
• You possess a bachelor's degree in Computer Science or equivalent practical experience.
• You have hands-on experience implementing the company's Data 360 (Data Cloud) or a comparable enterprise data platform across ingestion, modeling, identity, insight, activation, and operations.
• You exhibit hands-on expertise in 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.
• You are proficient in SQL, capable of constructing, debugging, and optimizing multi-table queries, joins, filters, transformations, and validation checks.
• You are adept in at least one implementation language used in enterprise delivery - Apex, Java, Python, or JavaScript/TypeScript - and are eager to learn the others as required.
• You understand data modeling, APIs, integration patterns, batch and streaming processing, and the practical distinctions between copied, federated, and Zero Copy data access.
• You have experience building retrieval systems for AI applications, such as vector databases, search indexes, embedding pipelines, or knowledge bases, and can articulate the design choices behind chunking, indexing, and relevance.
• You have implemented secure service-to-service integration patterns (OAuth, SAML, event-driven architectures) and are familiar with emerging protocols for agents like MCP and inter-agent communication.
• You can enforce secure data access and rigorously test it, covering permissions, entitlements, consent, user context, restricted populations, and negative test cases.
• You diagnose technical challenges methodically, using logs and query documentation to identify reproducible scenarios.
• You can implement against documented architecture, clearly communicate your construction to customer technical teams, and recognize questions that require an architect or security owner to answer.
• You have worked alongside customers, partners, professional services, or internal implementation teams in deadline-focused settings.
• You are enthusiastic about engaging directly with customer administrators, developers, data engineers, and architects.
• You hold relevant certifications in Agentforce, Data 360, and the company Platform.

Unleash Your Potential
When you join the company, you'll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best, and our AI agents enhance your impact so you can do your best. Together, we’ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love.