Job Overview

Key Responsibilities

• Represent quality in release readiness, design reviews, and incident reviews, and own the release go/no-go recommendation
• Define and enforce quality gates across the delivery pipeline, from pull request to production
• Drive shift-left by influencing architecture and design decisions to improve testability and reduce quality risk

• Define and report quality metrics that give engineering leadership release confidence
• Check that test coverage and automation actually catch issues before they reach production
• Expand shift-right beyond post-release smoke and sanity tests: continuous checks on critical customer journeys, quality validation during rollouts, and turning production incidents into automated regression tests

• Define what an SDET team looks like in the AI era: roles, skills, seniority mix, and how engineers work alongside AI agents
• Build and grow the QE team: hire, develop, and hold engineers to that standard

• Build test automation systems that scale with minimal maintenance, using AI across the QE lifecycle, from test generation to release validation
• Drive adoption of AI coding assistants and agents across the QE team, with clear guardrails on quality and ownership
• Set review standards for AI-generated tests and code
• Measure the impact of AI on test quality, coverage, speed, and effort

• Own the direction of test automation frameworks across UI, API, SDK, CLI, MCP, and integration layers
• Drive API-first testing, including contract and schema validation
• Define test strategies for complex cloud operations workflows that span multiple clouds, customer accounts, and distributed services
• Partner with SRE on test environments and cloud-based execution

Must Have

• 10-15 years in quality engineering or test automation, with 4+ years leading QE teams
• Track record of building or growing a QE team: defining roles, hiring, and setting the technical bar
• Strong hands-on programming in Python, Java, or JavaScript/TypeScript, and currently writing and reviewing automation code
• Hands-on experience testing applications and asynchronous, event-driven workflows built on AWS or Azure, with working knowledge of core AWS services and containers (ECS, EKS), and experience building cloud-native applications
• Hands-on experience with modern test automation frameworks (Playwright, Pytest, or equivalent), including designing frameworks for low maintenance at scale
• Hands-on use of AI across the QE lifecycle, built and used in a team's daily work, not proofs of concept, with standards for reviewing AI-generated code
• Strong expertise in API testing and contract or schema validation across APIs, SDK, CLI, and MCP
• Experience designing CI/CD quality gates (AWS CodeBuild and CodePipeline preferred, or equivalent)

Good to Have

• Quality engineering for agentic systems: end-to-end testing of agent workflows, tool calls, and outputs, including non-deterministic behavior and regression across releases
• Performance and scale testing for a multi-tenant SaaS platform, covering APIs and cloud operations workflows
• Working knowledge of observability tools (Datadog or similar)
• Exposure to security testing and compliance standards (OWASP, SOC 2, or similar)

Leadership & Soft Skills

• Top-notch written and verbal communication, from engineering teams to executive leadership, including quality reports that are short, specific, and honest about risk
• Owns release quality decisions, holds a clear position on quality and defends it with data in front of senior engineering and product leaders
• Influences engineering and product teams without relying on direct authority
• Thrives in a fast-paced, high-ownership environment

Education

• Bachelor's degree in Computer Science or a related field