Defect Equipment Systems Engineer – Automation & Data

About the role

This position focuses on supporting and enhancing automation systems that drive equipment performance, data analysis, and process optimisation within defect metrology and semiconductor manufacturing. You will build scalable automation solutions, improve system reliability, and enable faster decision‑making through better data integration and deployment practices.

Key responsibilities

• Design, develop and maintain automation solutions for equipment performance monitoring and process improvement.
• Create scalable Python tools and scripts to automate data extraction, validation, analysis and reporting.
• Write and optimise SQL queries to access and transform manufacturing and equipment data.
• Support cloud‑based application development and deployment for automation and analytics.
• Implement and maintain CI/CD pipelines in GitHub to improve software quality and deployment efficiency.
• Troubleshoot complex equipment and software issues, develop sustainable fixes and prevent recurrence.
• Lead cross‑functional improvement initiatives for DefMet systems and influence stakeholders to adopt innovative solutions.
• Collaborate with engineering, manufacturing and IT teams to enhance automation architecture, data flow and system reliability.

Required profile

• Level 8 degree (NFQ) or higher in a science or engineering discipline (e.g., Physics, Electrical/Electronic/Mechanical Engineering, Computer Science, etc.).
• At least 4 years of experience in an engineering, automation, equipment, data or software‑enabled manufacturing environment.
• Strong analytical and problem‑solving abilities with hands‑on troubleshooting experience.
• Proven ability to work autonomously and take ownership of solutions.

Required skills

• Python for automation, data processing or engineering analysis.
• SQL for data extraction, validation and analysis.
• GitHub for version control and CI/CD workflows.
• Experience with CI/CD pipeline implementation.
• Familiarity with cloud‑based development and deployment environments.
• Data analysis and visualisation tools such as JMP or Python libraries.