14 ott - Milano
Leadout Capital
h3About the Role /h3 br/ pbProduction implementation experience required. Builder mindset non-negotiable.br/ /b /p pSalesforce 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. /p pData 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. /p pYou’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.br/ /p pThis 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.br/ /p h3What You'll Do /h3 ul libBuild in customer environments. /b 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. /li libEngineer the data layer. /b 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 trade‑offs 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). /li libProve it works. /b Create representative test data and validate expected outcomes across happy paths, edge cases, access boundaries, hierarchy behavior, failure conditions, and customer‑scale volumes. /li libGround the agents. /b 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. /li libOrchestrate agent communication. /b 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. /li libConnect the ecosystem. /b Implement robust, scalable, and secure data integration patterns that connect Agentforce to a wide range of enterprise applications and enable communication between AI agents. /li libGovern the data. /b 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. /li libBuild for scale and security. /b 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. /li libDebug the hard problems. /b 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. /li libOwn implementation decisions. /b Compare viable techniques within the approved architecture, document measured trade‑offs, and recommend the most maintainable implementation to the responsible architect or technical lead. /li libAccelerate with AI. /b Use AI tooling - including Data 360 APIs and MCP Servers - to automate the build process and compress customers' time to value. /li libCo‑build and hand off. /b 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. /li libFeed the roadmap. /b Surface reproducible platform gaps and edge cases - with live evidence, impact,
and expected behavior - directly to Product and Support.br/ /li /ul h3You're Our Data 360 Forward Deployed Engineer If... /h3 ul liYou have b8+ years of experience in software engineering, data engineering, or technical implementation /b, including a production data solution you can explain in detail. /li liYou have a bbachelor's degree in Computer Science or equivalent /bpractical experience. /li liYou have a bhands‑on experience implementing Salesforce Data 360 (Data Cloud) or a comparable enterprise data platform /b across ingestion, modeling, identity, insight, activation, and operations. /li liYou have hands‑on experience building bdata pipelines and integrations /busing at least one enterprise data platform or technology, such as bSnowflake, Databricks, BigQuery, Redshift, Kafka, /b cloud object storage, or an enterprise MDM platform. /li liYou are bproficient in SQL /b and can build, debug, and optimize multi‑table queries, joins, filters, transformations, and validation checks. /li liYou are bfluent in at least one implementation language /b used in enterprise delivery - bApex, Java, Python, or JavaScript/TypeScript /b- and are willing to learn the others as needed. /li liYou understand bdata modeling, APIs, integration patterns, batch and streaming processing /b, and the practical differences between copied, federated, and Zero Copy data access. /li liYou’ve bbuilt retrieval systems /b for AI applications - bvector databases, search indexes, embedding pipelines, or knowledge bases /b - and can explain the design decisions behind chunking, indexing, and relevance. /li liYou’ve implemented secure bservice‑to‑service integration patterns (OAuth, SAML, event‑driven architectures) /b and are conversant in emerging agent protocols like bMCP and agent‑to‑agent /b communication. /li liYou can implement secure data access and rigorously test it, including bpermissions, entitlements, consent, user context, restricted populations, and negative test cases /b. /li liYou bdiagnose technical problems methodically /b, use logs and query evidence, isolate product behavior from configuration errors, and document a reproducible result. /li liYou can bimplement against a documented architecture /b, explain what you built to customer technical teams, and recognize when a question requires an architect or security owner. /li liYou have bdelivered /b alongside customers, partners, professional services, or internal implementation teams in bdeadline‑driven environments /b. /li liYou are excited to bwork directly with customer /b administrators, developers, data engineers, and architects. /li liYou have brelevant certifications /b in Salesforce Agentforce, Data 360, and the Salesforce Platfor /li /ul #J-18808-Ljbffr
14 ott - Milano
Idro 2000
14 ott - Ciriè
Filippo
14 ott - Udine
Gruppo Danieli
14 ott - Nova milanese
New Age