Quality Assurance Manager

QA Manager

Start: 2-3 weeks from date of offer

Location: Holland, MI

Salary Band: 130,000 - 150,000 per year

*Relocation Assistance Provided*

Interview Process: Likely 2 interviews - 1 virtual and 1 onsite(if local).

Quality Assurance Manager

About the Role

As a critical leader within our Technology organization, the Quality Assurance Manager will be

instrumental in ensuring the delivery of exceptional products and services to our customers.

This role demands a passion for quality, a deep understanding of software development

lifecycles, and experience in establishing robust QA processes for software, data, and AI

models, all within a DevOps environment.

Responsibilities:

 Strategic Leadership:

o Define and implement the overall QA strategy, encompassing software, data, and

AI, aligning with company goals and industry best practices.

o Champion a culture of quality across the organization, fostering collaboration and

a proactive approach to quality assurance within a DevOps framework.

 Software Quality Assurance:

o Develop and implement comprehensive test strategies for software applications,

covering functional, regression, performance, security, and usability testing, integrated within our CI/CD pipelines.

o Introduce and manage appropriate testing tools and automation frameworks to

enhance efficiency and coverage, ensuring seamless integration with our DevOps processes.

 Data Quality Management:

o Establish and enforce data quality standards across all data sources, pipelines,

and storage systems, incorporating data quality checks into our DevOps pipelines.

o Design and implement data quality checks, validation processes, and data governance procedures, ensuring alignment with our DevOps practices.

o Monitor data quality metrics, identify anomalies, and lead root cause analysis and

resolution efforts, collaborating closely with DevOps teams to address any

infrastructure-related issues.

 AI Model Quality Assurance:

o Define and implement testing strategies for AI models, encompassing accuracy,

bias detection, explainability, fairness, and robustness, integrating these tests into our model deployment pipelines.

o Establish metrics and benchmarks for evaluating AI model performance and

ensure compliance with ethical AI principles, working closely with data science

and DevOps teams to ensure responsible AI development and deployment.

 Team Leadership & Collaboration:

o Build, manage, and mentor a high-performing team of QA professionals,

providing guidance, training, and development opportunities, fostering a DevOps

mindset within the team.

o Collaborate closely with product management, software development, data

science, and operations teams to ensure seamless integration of quality

throughout the product lifecycle, promoting a culture of shared responsibility for

quality within the DevOps framework.

 Continuous Improvement:

o Stay abreast of emerging technologies, industry trends, and best practices in

software quality, data quality, and AI model testing, particularly in the context of

DevOps and continuous delivery.

o Drive continuous improvement initiatives to enhance QA processes,

methodologies, and tools, leveraging automation and DevOps principles to

optimize efficiency and effectiveness.

Qualifications:

 Bachelor's degree in Computer Science, Engineering, or a related field.

 8+ years of experience in Quality Assurance, with at least 3+ years in a leadership role,

ideally within a DevOps environment.

 Proven experience in building and managing QA teams, ideally in a fast-paced, agile

environment, with a strong understanding of DevOps principles and practices.

 Strong understanding of software development methodologies (Agile, DevOps) and

experience with CI/CD practices, including experience with implementing and managing

automated testing within CI/CD pipelines.

 Experience with test automation tools and frameworks, as well as performance and

security testing tools, with a focus on tools that integrate well with DevOps workflows.

 Experience with data quality management, data governance, and data validation

techniques, ideally in a DevOps environment.

 Familiarity with AI/ML concepts, model evaluation metrics, and bias detection

methodologies, with experience in integrating AI model testing into DevOps pipelines.

 Excellent communication, interpersonal, and stakeholder management skills, with the

ability to effectively collaborate with technical and non-technical stakeholders within a

DevOps culture.