Why This Role Stands Out
As a Principal Quality Engineering Lead at Crea Services LLC, you'll drive impactful, large-scale initiatives and shape the future of quality engineering through technical leadership and strategic influence. This role is ideal for a curious and results-oriented engineer who thrives on solving complex problems and mentoring others, offering significant opportunities for professional growth within a reputable company. Apply now to leverage your expertise and make a tangible difference.
Quick Overview
Job Description
Role: Quality Engineer Level 3 / Principal Quality Engineering Lead
Location: Blue Ash, OH (5 Day Onsite)
About the Role
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.
Key Responsibilities
Lead large-scale technical initiatives across multiple engineering teams, translating business objectives into actionable roadmaps and driving sustainable solutions to recurring quality, release, and operational challenges.
Define and drive Quality Engineering strategy, standards, test architecture, automation, CI/CD, release quality, observability, test environments, risk management, and production readiness.
Provide technical leadership and architectural guidance by reviewing code, automation frameworks, technical designs, and architectural tradeoffs, while guiding teams without being the primary implementer.
Drive AI-enabled Quality Engineering by identifying practical AI opportunities across the SDLC, evaluating emerging AI capabilities, improving engineering productivity and test effectiveness, and promoting responsible AI adoption with measurable outcomes.
Solve complex and ambiguous problems by identifying systemic issues across technology, processes, and organizations and creating clear, scalable solutions rather than temporary fixes.
Influence and elevate engineering practices organization-wide by partnering with Engineering, Product, Architecture, SRE, Release Management, and QE leaders; mentoring senior engineers/technical leads; and leading through influence rather than authority.
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.
Tech Stack / Environment:
- React, Java/Spring microservices, MQTT, Azure/ISC, automated CI/CD, contract testing, mutation testing, synthetic monitoring, and automated quality gates.
- Specific stack experience is less important than strong modern engineering / QE fundamentals and the ability to quickly work across technologies.
Top 3 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.
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