QA Automation Technical Lead

Golden Technology; Cincinnati (OH); On-site; USD 150,000 - 210,000; Full time

Job summary

Golden Technology is seeking a Principal Quality Engineering Lead to drive transformational quality outcomes across product, engineering, release, and operations teams.

The role emphasizes strategic leadership without direct people management, focusing on influence, coaching, and technical leadership to implement AI-enabled practices and robust delivery governance.

Qualifications

• 10+ years in software engineering, quality engineering, platform engineering, or related disciplines.
• Experience leading large-scale technical initiatives without direct authority.
• Strong understanding of modern software delivery practices and distributed systems.
• Deep understanding of automation frameworks and CI/CD practices.
• Knowledge of release quality, risk assessment, defect prevention, and production readiness.
• Experience applying AI tools to engineering workflows.

Responsibilities

• Lead cross-functional initiatives spanning multiple engineering teams and org boundaries.
• Translate business objectives into actionable roadmaps and plans.
• Identify systemic causes behind recurring quality, release, or operational challenges.
• Drive sustainable, long-term solutions rather than quick fixes.
• Define quality engineering strategies, standards, and architectures.
• Guide teams on test strategy, automation, release quality, observability, environments, and risk management.
• Review designs and implementation approaches and influence architecture decisions through a quality lens.
• Identify opportunities to leverage AI across the software development lifecycle and ensure responsible adoption.

Skills

Technical Leadership

Quality Engineering

AI in Eng

Distributed Systems

CI/CD

Release Management

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

• 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.

Technical Depth

Must be able to:

• Review automation frameworks and technical designs.
• Guide engineering teams without becoming the primary implementer.

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 a 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.

Solve 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.