Full Stack Principal Software Engineer - Event Technology

Salesforce Salesforce · Enterprise · Seattle, WA +4

Salesforce is seeking a Principal Full Stack Software Engineer for their Event Technology team. This role focuses on architecting and leading the technical vision for event platforms and public websites, with a strong emphasis on integrating AI agents into human workflows and leveraging AI tools for development. The engineer will be responsible for the full software development lifecycle, including design, implementation, testing, deployment, and operations, while mentoring other engineers and ensuring scalability, performance, and observability.

What you'd actually do

  1. Lead the architectural strategy and direction for the Event Technology and websites portfolio, ensuring alignment with Marketing Technology’s overall technology vision and business goals.
  2. Architect, design, implement, test, and deliver highly scalable applications and services.
  3. Operate optimally in the hybrid engineering model where engineers are encouraged to craft and complete the vital work to ensure quality in their code and other engineers.
  4. Strategically plan, design, and execute the implementation of highly scalable solutions that meet current and future business needs.
  5. Collaborate with Architects, Lead Engineers, Product, UX and cross-functional teams in the application design process, contributing innovative ideas and technical expertise.

Skills

Required

  • 10+ years of professional software development experience in designing, building, scaling, and maintaining production systems.
  • Proven experience in a technical leadership role, including architecture, design, and implementation of complex software systems.
  • Strong understanding and embodiment of service ownership principles and skills; expertise in building observable and resilient systems with the ability to proactively prevent issues before our customers even notice.
  • Experience with domain driven design.
  • Proven ability to mentor team members to support their understanding and growth of software engineering concepts and aid in their technical development.
  • Experience developing front-end and back-end software, preferably including JavaScript with TypeScript, with frameworks such as React, runtimes including Node.js and CSS frameworks such as Tailwind or Sass.
  • Experience building high-scale microservices on AWS (preferred), GCP, or other public cloud substrates. Examples of AWS services we use include Lambda, DynamoDB, SNS, SQS, EventBridge, API Gateway, and more.
  • Expertise in consuming (and ideally building) GraphQL and RESTful APIs and strong understanding of API security best practices.
  • A demonstrated, genuine AI-first approach to engineering. Using AI to move faster, build fluency across the stack, and contribute well beyond your core specialty.
  • Proven experience leveraging AI tools (Claude, Cursor, etc.) in your daily workflows and the ability to uplevel other engineers’ usage of AI tools.
  • Advanced prompt engineering skills and the ability to write precise, structured prompts and cultivate the system context that makes AI outputs reliable, secure, and production-ready.

Nice to have

  • Knowledge of mobile and cross-platform/browser test automation.
  • Experience with Snowflake, Google Analytics, and configuring CDNs such as Akamai.
  • Proven ability to collaborate closely with cross-functional teams, including product managers, designers, and other engineering teams, to deliver exceptional user experiences.

What the JD emphasized

  • AI agents integrate seamlessly into human workflows
  • shared system context, an explicit repository of system designs, constraints, and standards that enables AI to operate accurately and reliably
  • Critically evaluate code (Human or AI-generated)
  • A demonstrated, genuine AI-first approach to engineering
  • Using AI to move faster, build fluency across the stack, and contribute well beyond your core specialty
  • Advanced prompt engineering skills
  • cultivate the system context that makes AI outputs reliable, secure, and production-ready

Other signals

  • AI agents integrate seamlessly into human workflows
  • shared system context, an explicit repository of system designs, constraints, and standards that enables AI to operate accurately and reliably
  • Critically evaluate code (Human or AI-generated)
  • A demonstrated, genuine AI-first approach to engineering
  • Using AI to move faster, build fluency across the stack, and contribute well beyond your core specialty
  • Advanced prompt engineering skills
  • cultivate the system context that makes AI outputs reliable, secure, and production-ready