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About the Role

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. 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.

Responsibilities

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.
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).
Create representative test data and validate expected outcomes across happy paths, edge cases, access boundaries, hierarchy behavior, failure conditions, and customer-scale volumes.
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.
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.
Implement robust, scalable, and secure data integration patterns that connect Agentforce to a wide range of enterprise applications and enable communication between AI agents.
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.
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.
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.
Compare viable techniques within the approved architecture, document measured tradeoffs, and recommend the most maintainable implementation to the responsible architect or technical lead.
Use AI tooling - including Data 360 APIs and MCP Servers - to automate the build process and compress customers' time to value.
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.
Surface reproducible platform gaps and edge cases - with live evidence, impact, and expected behavior - directly to Product and Support.

Requirements

5+ years of experience in software engineering, data engineering, or technical implementation, including a production data solution you can explain in detail.
Hands-on experience implementing Salesforce Data 360 (Data Cloud) or a comparable enterprise data platform across ingestion, modeling, identity, insight, activation, and operations.
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.
Proficient in SQL and can build, debug, and optimize multi-table queries, joins, filters, transformations, and validation checks.
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.
Understand data modeling, APIs, integration patterns, batch and streaming processing, and the practical differences between copied, federated, and Zero Copy data access.
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.
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.
Can implement secure data access and rigorously test it, including permissions, entitlements, consent, user context, restricted populations, and negative test cases.
Diagnose technical problems methodically, use logs and query evidence, isolate product behavior from configuration errors, and document a reproducible result.
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.
Delivered alongside customers, partners, professional services, or internal implementation teams in deadline-driven environments.
Excited to work directly with customer administrators, developers, data engineers, and architects.
Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.

Skills

Data 360 configuration
SQL
Apex
Flow
Python
REST Query API
Interaction SDK
Salesforce DX
CLI
Git
Data Kits
Deployment tooling
Data pipelines
Data integrations
Snowflake
Databricks
BigQuery
Redshift
Kafka
Cloud object storage
Enterprise MDM platform
Data modeling
APIs
Integration patterns
Batch processing
Streaming processing
Vector databases
Search indexes
Embedding pipelines
Knowledge bases
OAuth
SAML
Event-driven architectures
Model Context Protocol (MCP)
Agent-to-agent communication
Message queues
Distributed systems
Salesforce Agentforce
Salesforce Data 360
Salesforce Platform

Location

California
New York
Boston
Chicago
Seattle
Washington DC

Work Type

Full-time

Experience Level

5+ years

Education Level

Bachelor's degree in Computer Science or equivalent practical experience

Salary/Compensations

$88,970 - $287,910 annually
$97,860 - $316,750 per year in California and New York, and select cities in the metropolitan areas of Boston, Chicago, Seattle, and Washington DC

Benefits

Time off programs
Medical
Dental
Vision
Mental health support
Paid parental leave
Life insurance
Disability insurance
401(k)
Employee stock purchasing program

About the Company

Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all. Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce.

Equal Opportunity

Salesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment.
At Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that’s inclusive, and free from discrimination.
Know your rights: workplace discrimination is illegal.
Any employee or potential employee will be assessed on the basis of merit, competence and qualifications – without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law.
This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey.
It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between.
Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit.
The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.