We are seeking a Defect Analysis Systems Engineer to lead the design, development, and optimization of data systems that drive yield improvement in semiconductor manufacturing. In this role, you will own and support systems, data workflows, and automation solutions that enable defect analysis, process optimization, and operational excellence. You will combine software engineering, data analysis, problem-solving, and manufacturing domain expertise to build scalable solutions that improve defect detection, root-cause analysis, trend identification, and analysis workflow efficiency. You will collaborate with engineering, manufacturing, quality, and automation teams to investigate complex process and equipment issues, develop automation frameworks, and implement sustainable solutions that reduce variation and prevent recurring issues. You will also identify and implement innovative analytics across diverse big data sources and apply AI/ML concepts to enhance classification, prediction, and analysis workflows. Across all aspects of the role, you will help drive adoption of new capabilities and process improvements across teams to ensure lasting impact.
Minimum Requirements
- Level 8 degree (NFQ) or higher in a Science or Engineering discipline, such as Physics, Chemistry, Materials Science, Electrical, Electronic, or Mechanical Engineering, Chemical Engineering, Mechatronics, Nanotechnology, Mathematics, Computer Science, or related fields.
- 4+ years of experience in an engineering, automation, equipment, data, process, or software-enabled manufacturing environment.
- Strong analytical and problem-solving skills, with experience troubleshooting complex systems and data issues.
- Practical experience with Python for automation, data processing, data analysis, or engineering problem solving.
- Strong working knowledge of SQL for data extraction, validation, and analysis.
- Experience with data analysis and visualization tools such as JMP, Power BI, Python libraries, or similar tools.
- Experience using GitHub for version control and CI/CD workflows.
Preferred Requirements
- Experience in semiconductor manufacturing or related high-tech industries.
- Experience with defect analysis, yield analysis, defect reduction methodologies, or process improvement.
- Familiarity with equipment automation systems, industrial software, or manufacturing data platforms.
- Exposure to cloud-native application development, APIs, automated deployment pipelines, or modern software engineering practices.
- Experience with customer service mindset and experience working with external and internal partners.
- Knowledge of statistical methods, DOE, SPC, and other data-driven manufacturing problem-solving approaches.
- Experience with AI/ML applications for data classification, anomaly detection, or predictive analytics in manufacturing.
Experienced Hire
Shift 1 (Ireland)
Ireland, Leixlip
All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.
N/A
We offer a total compensation package that ranks among the best in the industry.
Annual Salary Range for this job which could be performed in Ireland is: 59,200.00 € - 111,300.00 €

The compensation range displayed in this posting reflects the minimum and maximum target compensation for this position. Within this range, individual pay is determined by job related skills, experience, and relevant education, training. As part of our total compensation approach, the final offer may include additional pay components and benefits. Your recruiting team will share the confirmed base salary along with any applicable total compensation elements upon employment offer delivery.
Work Model for this Role
This role will require an on-site presence. * Job posting details (such as work model, location or time type) are subject to change.
