Data 360 - Lead/ Principal Forward Deployed Engineer

Salesforce.com; Dubai; On-site; AED 380,000 - 540,000; Full time

Job summary

Salesforce.com is seeking Forward Deployed Engineers focused on Data 360 to implement in real enterprise environments, writing SQL, Python and Apex, and building ingestion pipelines with identity resolution. You will ship working implementations from sandbox through production in customer environments.

You have 8+ years of software or data engineering experience, a CS degree, and hands-on Data 360 or similar platforms across ingestion, modeling, and activation.

Qualifications

• Production implementation experience required.
• Builder mindset non-negotiable.
• Hands-on technical builder in Salesforce Data 360 deployments across ingestion, modeling, identity, insight, activation, and operations.
• Experience implementing Salesforce Data 360 or comparable enterprise data platform.

Responsibilities

• Build in customer environments using Data 360 configuration SQL Apex Flow Python and the REST Query API.
• Engineer the data layer for ingestion harmonization and identity resolution across batch and streaming sources.
• Prove it works by creating representative test data and validating outcomes across happy paths and edge cases.
• Ground the agents by designing AI data integration layers and MCP protocol usage.
• Connect the ecosystem with secure data integration patterns and scalable workflows.
• Govern data with secure integration, transformation and governance across Salesforce CRM and external systems.
• Debug ingestion failures, mapping defects, schema drift, and API errors with thorough log analysis.
• Own implementation decisions and recommend maintainable solutions to architects.
• Accelerate with AI using Data 360 APIs and MCP to automate build processes.
• Co-build and hand off with customer teams, pairing reviews and runbooks for reproducibility.

Skills

SQL

Python

Apex

Data engineering

Ingestion pipelines

Identity resolution

Testing & debugging

Education

Bachelor's degree in Computer Science

Tools

Salesforce DX CLI

REST Query API

Data Kits

Git

Job description

About the Role

This 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

• Production implementation experience required
• Builder mindset non-negotiable
• 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 You 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

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

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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 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 e g data mesh data fabric
• 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 Retrieval-Augmented Generation 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 MCP 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 CRM Data Cloud 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 OAuth SAML so that all agent actions are secure and comply with enterprise security policies
• Debug the hard problems 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
• Own implementation decisions Compare viable techniques within the approved architecture document measured tradeoffs and recommend the most maintainable implementation to the responsible architect or technical lead
• Accelerate with AI Use AI tooling - including Data 360 APIs and MCP Servers - 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 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
• Feed the roadmap Surface reproducible platform gaps and edge cases - with live evidence impact and expected behavior - directly to Product and Support

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You have 8+ years of experience in software engineering, data engineering, or technical implementation, including a production data solution you can explain in detail.You have a bachelor's degree in Computer Science or equivalent practical experience.You have hands-on experience implementing Salesforce Data 360 (Data Cloud) or a comparable enterprise data platform across ingestion, modeling, identity, insight, activation, and operations.You have hands-on experience 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 and can build, debug, and optimize multi-table queries, joins, filters, transformations, and validation checks.You are fluent in at least one implementation language used in enterprise delivery - Apex, Java, Python, or JavaScript/TypeScript - and are willing to learn the others as needed.You understand data modeling, APIs, integration patterns, batch and streaming processing, and the practical differences between copied, federated, and Zero Copy data access.You’ve built retrieval systems for AI applications - vector databases, search indexes, embedding pipelines, or knowledge bases - and can explain the design decisions behind chunking, indexing, and relevance.You’ve implemented secure service-to-service integration patterns (OAuth, SAML, event-driven architectures) and are conversant in emerging agent protocols like MCP and agent-to-agent communication.You can implement secure data access and rigorously test it, including permissions, entitlements, consent, user context, restricted populations, and negative test cases.You diagnose technical problems methodically, use logs and query evidence, isolate product behavior from configuration errors, and document a reproducible result.You can implement against a documented architecture, explain what you built to customer technical teams, and recognize when a question requires an architect or security owner.You have delivered alongside customers, partners, professional services, or internal implementation teams in deadline-driven environments.You are excited to work directly with customer administrators, developers, data engineers, and architects.You have relevant certifications in Salesforce Agentforce, Data 360, and the Salesforce Platform.