Quality Engineer

Day to Day Responsibility

• Own and execute product quality assurance (PQA) activities across the product lifecycle, including quality planning, risk assessment, qualification, change control, and ongoing performance monitoring.
• Establish, maintain, and improve QC plans, inspection methods, sampling strategies, acceptance criteria, and reaction plans for incoming materials, in-process production, and finished products.
• Monitor and improve process capability and stability using statistical tools such as SPC, capability analysis, MSA, control charts, trend analysis, DOE, and appropriate statistical modeling.
• Collaborate with Manufacturing, Process Engineering, Equipment Engineering, and other functions to define and maintain quality standards and controls, identify critical process parameters and sources of variation, and improve process capability, robustness, and product quality.
• Lead or support investigation of internal and customer quality issues using structured problem-solving methods, identify root causes, and ensure timely and effective corrective and preventive actions.
• Evaluate product and process changes, identify quality risks, and ensure appropriate validation and control measures are implemented before release.
• Audit quality systems and manufacturing processes, identify gaps, and drive sustainable corrective actions and compliance with applicable requirements.
• Prepare and communicate quality performance, risks, trends, and improvement priorities to stakeholders in a clear, timely, and proactive manner.
• Analyze large and complex manufacturing and quality datasets to identify trends, correlations, abnormal patterns, sources of variation, and improvement opportunities.
• Integrate product test results with process parameters, equipment conditions, and material information to support fact-based root cause identification, risk prediction, and process optimization.
• Develop effective data visualizations, dashboards, and analytical tools to improve quality monitoring, management review, decision-making, and early detection of product or process risks.
• Identify and implement opportunities to digitalize or automate manual quality processes, reporting, data review, and knowledge management.
• Explore and apply AI-enabled solutions, in collaboration with relevant functions, to improve quality monitoring, data analysis, problem solving, reporting efficiency, and continuous improvement.
• Provide on-site quality support, coaching, and training to strengthen quality awareness, standard execution, and problem-solving capability.
• Lead or participate in continuous improvement and Performance Excellence projects using DMAIC, Lean, or other appropriate methodologies.
• Continuously build technical and quality knowledge, share learning, and adapt quickly to evolving products, processes, and business needs.

Education & Experience

• Bachelor’s degree or above in Engineering, Quality, Materials Science, Industrial Engineering, Data Science, or a related technical discipline.
• At least 3 years of experience in Quality Engineering, Process Engineering, Manufacturing Engineering, or a closely related technical function within a manufacturing environment.
• Hands-on experience in PQA, QC, process control, process engineering, or manufacturing quality improvement is strongly preferred.
• Demonstrated experience or strong potential in process understanding, process control, capability analysis, structured experimentation, and manufacturing quality improvement.
• Experience in fiber manufacturing or another process-intensive manufacturing environment is preferred; experience in data analytics, digital manufacturing, automation, or AI applications is a plus.

Desired Skills

• Strong knowledge of PQA and QC practices, including quality planning, control plans, inspection and testing, nonconformance management, and change control.
• Proficiency in SPC, process capability analysis, MSA, FMEA, 8D, root cause analysis, and corrective and preventive action.
• Strong quantitative and analytical capability, with demonstrated ability to convert manufacturing and quality data into actionable insights and data-driven decisions.
• Ability to connect process inputs, equipment conditions, material variation, and product quality outputs through engineering and statistical analysis.
• Proficiency in Minitab or equivalent statistical software; experience with Power BI, Python, SQL, or equivalent data analysis and visualization tools is preferred.
• Knowledge of DOE, multivariate analysis, regression, anomaly detection, and other applicable analytical methods is preferred.
• Ability to develop and improve quality systems, data-driven process controls, monitoring methods, and reaction plans.
• Experience with Performance Excellence, Lean, or Six Sigma methodologies is preferred.
• Strong interest in applying digital, automation, and AI technologies to manufacturing quality; demonstrated learning agility with emerging tools is required.
• Experience in manufacturing digitalization, workflow automation, AI, machine learning, or quality knowledge management is highly preferred.
• Effective project management and cross-functional influence skills.

Soft Skills

• Proactive, clear, and timely communication, with the ability to align and collaborate effectively across functions and organizational levels.
• Strong learning agility and curiosity, with a demonstrated ability to continuously build technical knowledge and apply new learning to business needs.
• Self-motivated, accountable, and able to work independently, manage priorities, and perform effectively under pressure.
• Strong ownership, initiative, and follow-through in identifying risks, escalating concerns, and driving issues to closure.
• Demonstrated curiosity and change leadership, with the ability to translate new analytical or digital concepts into practical manufacturing applications.
• Strong written and verbal communication skills in Mandarin and English.