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Quality Engineer Level 3

Job in Cincinnati, Hamilton County, Ohio, 45208, USA
Listing for: Strategic Data Systems, Inc.
Full Time position
Listed on 2026-10-17
Job specializations:
  • Software Development
    Software Architect, Software Project Mgr/ Lead
Salary/Wage Range or Industry Benchmark: 140000 - 170000 USD Yearly USD 140000.00 170000.00 YEAR
Job Description & How to Apply Below
Careers at SDS Quality Engineer Level 3

Contract to Hire

Job Title:
Principal Quality Engineer

Location:
Cincinnati, OH (onsite 5x/wk)

Years of

Experience:

10+

Contract to Hire - must be able to convert to FTE without sponsorship

TOP

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.
What You’ll Do

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.
Technical Depth
  • Review code effectively.
  • Review automation frameworks and technical designs.
  • Evaluate architectural tradeoffs.
  • 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…
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