Senior Backend Engineer for AI Data Platform

salesforce.com, inc.

Seattle (WA)

On-site

USD 148,500 - 313,700

Full time

14 days+

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Job summary

Salesforce seeks a senior backend engineer to shape the Agentforce Operations platform. You will architect and build the integration backbone, optimize data pipelines, and deliver scalable APIs that connect to MuleSoft, Flow, and Data Cloud.

You’ll own cross-team interfaces and guide system design with an AI-first approach. You’ll work on high-impact backend challenges, decompose complex domains, and mentor engineers while collaborating with Product Managers to deliver enterprise-grade

Qualifications

3+ years (Senior), 5+ years (Lead), 8+ years (Principal) of backend engineering experience.
Related technical degree required.
Expert in data modeling, DB performance tuning, state management, API design, concurrency, fault tolerance, and scaling.
Proven experience designing and maintaining production-grade data pipelines (ETL/ELT).
Expertise in at least one modern language (Java, Python, Go).
Experience designing/maintaining robust REST APIs and Webhooks.
Deep knowledge of distributed systems and high-availability architectures.
Experience leading technical initiatives as a senior engineer or lead.
Strong collaboration with Product Managers and other teams.
Familiarity with Salesforce integration technologies (MuleSoft, Data Cloud).

Responsibilities

Architect and implement integrations between core platform and Salesforce ecosystem products.
Lead scalable, reliable data export pipelines; integrate internal data sources with Data Cloud or Informatica.
Optimize ETL pipelines to support daily self-serve data synchronization.
Design and build efficient, customer-facing APIs and webhooks, including GraphQL endpoint abstractions.
Architect and deliver high-quality, scalable code for data integration and platform features.
Drive technical decision-making and project execution as a primary contributor.
Perform thorough code reviews and mentor engineers on performance, security, and scalability.
Collaborate with Product Management and other teams to define requirements and solutions.
Build and ship production-grade software with AI as a core development factor.
Design systems to enable AI agents to operate within human workflows.
Contribute to shared system context of designs, constraints and standards.
Critically evaluate code for correctness, quality, security, and performance.
Lead by example and raise engineering quality bars across teams.
Set technical direction for data integration, ETL, and backend architecture.
Identify architectural risks and drive scalability improvements.
Improve system performance, observability, and operability.