Automation Test Lead

Job Description

The Principal Quality Engineering Lead is a senior individual contributor responsible for driving transformational quality engineering outcomes across product, engineering, release, and operations teams. This role requires a unique blend of technical depth, systems thinking, organizational influence, and relentless curiosity. The individual will lead complex initiatives spanning quality engineering, test architecture, release readiness, environment strategy, AI-enabled engineering practices, and software delivery governance.

Success in this role is measured by business outcomes, not activity metrics. The ideal candidate can take an ambiguous problem, define a strategy, align stakeholders, and drive execution with minimal oversight. This role does not have direct people management responsibilities but is expected to operate at a leadership level through influence, coaching, and technical leadership.

Required Experience

Technical Leadership

• 10+ years in software engineering, quality engineering, platform engineering, or related technical disciplines.
• Experience leading large-scale technical initiatives without direct authority.
• Strong understanding of modern software delivery practices and distributed systems.

Quality Engineering Expertise

• Experience defining test strategies across multiple test levels.
• Deep understanding of automation frameworks and CI/CD practices.
• Knowledge of release quality, risk assessment, defect prevention, and production readiness.

AI Fluency

• Demonstrated experience applying AI tools to engineering workflows.
• Ability to evaluate AI opportunities beyond simple code generation.
• Understanding of strengths, limitations, risks, and governance considerations associated with AI-assisted engineering.

Key Responsibilities

Drive Complex Cross-Functional Outcomes

• Lead initiatives that span multiple engineering teams and organizational boundaries.
• Translate broad business objectives into actionable roadmaps.
• Identify systemic causes behind recurring quality, release, or operational challenges.
• Drive sustainable solutions rather than temporary fixes.

Technical Leadership

• Define quality engineering strategies, standards, and architectures.
• Guide teams on test strategy, automation, release quality, observability, test environments, and risk management.
• Review designs, frameworks, and implementation approaches.
• Influence architecture decisions through quality and reliability lens.

AI-Enabled Quality Engineering

• Identify and implement opportunities to leverage AI across the software development lifecycle.
• Evaluate emerging AI capabilities and determine practical applications.
• Improve engineering productivity, test effectiveness, and delivery speed through AI-assisted workflows.
• Promote responsible and measurable AI adoption.

Solving Ambiguous Problems

• Investigate complex technology, process, and organizational issues.
• Challenge assumptions and drive root-cause analysis.
• Build consensus among diverse stakeholders.
• Create clarity where none exists.

Organizational Influence

• Partner with Engineering, Product, Architecture, SRE, Release Management, and Quality Engineering leaders.
• Mentor senior engineers and technical leads.
• Lead through influence rather than authority.
• Elevate engineering practices across the organization.

Technical Depth

Must be able to review code effectively, review automation frameworks and technical designs, evaluate architectural tradeoffs, guide engineering teams without becoming the primary implementer.

Skills

• Technical Quality Engineering Leadership - Strong background in modern QE, automation, CI/CD, release quality, and the SDLC, with enough technical depth to review code, frameworks, and architecture.
• Cross-Functional Influence & Problem Solving - Able to work across multiple teams, solve ambiguous problems, identify root causes, and drive change without having direct authority.
• AI Fluency in Engineering - Comfortable using AI to evaluate ideas, solve technical problems, improve testing and engineering practices, and work effectively across different technology stacks.

Technical Environment: React, Java/Spring microservices, MQTT, Azure/ISC, automated CI/CD, contract testing, mutation testing, synthetic monitoring, and automated quality gates.