Sr Quality Assurance Engineer (customer Facing)

Workday Workday · Enterprise · San Francisco, CA

This role is for a Sr. Quality Assurance Engineer on the Pipedream team, which enables AI agents to connect to various software and services. The role is customer-facing, focusing on supporting developers using the platform, debugging complex integration issues, and improving support processes. It requires strong technical skills in JavaScript or Python, API troubleshooting, and a working knowledge of AI concepts like LLMs and agents. The goal is to ensure the reliability and evolution of the platform based on real-world feedback.

What you'd actually do

  1. Supporting software developers across the full range of issues, from troubleshooting technical errors (including authentication failures, malformed requests, and rate limits) to billing, feature requests, and general account questions
  2. Owning the most complex integration issues and sensitive partner escalations, reviewing how teammates approach difficult problems, pairing on debugging, and helping the team grow into more confident operators
  3. Identifying technical and usability issues with connectors and collaborating with product and engineering to prioritize and resolve them
  4. Driving continuous improvement of support and quality processes, including playbooks, internal tooling, documentation, and automations to help the team scale

Skills

Required

  • 7+ years of related work experience in QA, solutions or support engineering, forward deployed engineering, or a closely related function working with technically sophisticated software developers
  • Strong hands-on technical skills, with the ability to develop in JavaScript or Python and reason about production code
  • Proficiency working with APIs, troubleshooting API request failures, and using API documentation
  • Excellent customer service and communication skills, with the ability to credibly engage both software developers and non-technical users in a customer-facing capacity
  • Working knowledge of AI concepts including LLMs, agents, tool calling, evals, and MCP
  • Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent practical experience)

Nice to have

  • Passion for AI products, with hands-on experience using AI coding tools (e.g., Claude Code, Codex, Cursor) and building AI agents or agentic workflows
  • Experience supporting platforms with large, complex surface areas and managing competing priorities in a customer-facing environment
  • Experience mentoring or leading other team members
  • Strong analytical thinking, with the ability to break down complex technical problems into their fundamental parts, identify patterns across support issues, and use logical reasoning to drive root cause analysis and inform process improvements
  • A detail-oriented mindset, with the ability to spot errors or inconsistencies that others may overlook, manage complex information across multiple issues and accounts, and maintain a high level of accuracy and consistency in your work
  • Strong collaboration and interpersonal skills, with the ability to work effectively across product, engineering, and support teams, leverage diverse perspectives to solve problems, and foster a culture of mutual respect and shared learning
  • A genuine passion for learning and intellectual curiosity — you enjoy picking up new technologies and diving into unfamiliar problem spaces
  • Comfortable working asynchronously across multiple time zones on a geographically distributed team

What the JD emphasized

  • customer-facing
  • complex problems
  • complex integration issues
  • sensitive partner escalations
  • technically sophisticated software developers
  • troubleshooting technical errors
  • complex technical problems

Other signals

  • AI platform for managing people, money, and agents
  • enables AI agents to connect to the software and services
  • building AI agents and workflow products
  • intersection of developer tools and AI
  • next generation of intelligent software
  • Working knowledge of AI concepts including LLMs, agents, tool calling, evals, and MCP
  • hands-on experience using AI coding tools
  • building AI agents or agentic workflows