Senior Engineer, Manufacturing Engineering – Process Control

Job Description

This is an individual contribution role as a Plating Process Control Engineer in manufacturing engineering operations responsible for monitoring and controlling plating processes to ensure quality and yield targets while implementing process improvements and optimization initiatives. The role encompasses managing plating bath chemistry and refresh schedules, troubleshooting process deviations, conducting statistical analysis to support process changes, and ensuring compliance with contamination control procedures. The engineer is also expected to leverage data analytics and machine learning techniques to establish linkages between Key Process Input Variables (KPIVs) and Key Process Output Variables (KPOVs), enabling predictive process control and data-driven decision making. Key technical competencies include understanding electroplating principles and chemistry, knowledge of semiconductor manufacturing processes, experience with process control systems and data analysis, and familiarity with cleanroom operations and safety protocols. The engineer also participates in FMEA activities, risk assessments, and supports change control processes such as PCN and TECN to maintain operational excellence in plating operations.

ESSENTIAL DUTIES AND RESPONSIBILITIES:

• Focal person for plating facilities related issues, ie plating DI water trend monitoring, LPC and
• contamination
• Focal person of plating chemical planning and inventory
• Focal person for plating pretreatment, strip line, filter change and descaling.
• Monitor and control plating processes to ensure quality and yield targets
• Implement process improvements and optimization initiatives
• Troubleshoot process deviations and implement corrective actions, which include thickness control, anodic protection
• Troubleshoot line to line variation and implement corrective actions
• Ensure compliance with contamination control procedures
• Participating in FMEA activities and risk assessments
• Monitor and improve plating OEE performance
• Implement process optimizations to reduce speed losses and improve efficiency
• Track and analyze OEE metrics to identify improvement opportunities
• Support implementation of process parameter revisions to enhance OEE
• Identify, define, and document KPIVs and KPOVs for plating processes and establish data-driven linkages between them
• Develop and maintain process monitoring dashboards using data analytics tools to visualize KPIV-KPOV relationships
• Apply machine learning models (e.g., regression, classification, anomaly detection) to predict process outcomes and enable proactive process control
• Collaborate with data engineers or IT teams to integrate manufacturing data from equipment and sensors into analytics platforms
• Utilize statistical process control (SPC) and advanced analytics to detect processes that drift and trigger timely corrective actions

Qualifications

REQUIRED:

• Must have a minimum of a Degree in Chemical Engineering or any engineering background

PREFERRED:

• Fresh graduate or 1 - 3 years of experience in manufacturing/ production environment.
• Able to work in cleanroom environment.
• Must be able to use computer for communication (email / Microsoft Office applications & etc).
• Able to communicate in English.

• Exposure to data analytics tools such as Python, R, JMP, Minitab, Power BI, or Tableau
• Basic understanding of machine learning concepts and their application in manufacturing process control
• Experience or academic exposure to KPIV/KPOV identification and correlation analysis

SKILLS

• Excellent Project Management skills a strong communicator at all levels from Management to technical teams on the floor.
• Self-motivated and goal oriented and will have the ability to work equally well in a team environment as well as individually.
• Very independently and less supervision required.
• Problem solution tools (6 Sigma, DOE, FMEA, 8D).
• Data Analytics: Ability to collect, clean, and analyze large process datasets to derive actionable insights
• KPIV/KPOV Analysis: Proficiency in identifying critical input and output variables and establishing statistical correlations
• Machine Learning: Familiarity with supervised/unsupervised learning models for predictive process control and anomaly detection
• Data Visualization: Ability to build dashboards and reports to communicate process trends to engineering and management teams
• Statistical Tools: Proficiency in SPC, regression analysis, multivariate analysis, and Design of Experiments (DOE)

Additional Information

WD thrives on the power and potential of diversity. As a global company, we believe the most effective way to embrace the diversity of our customers and communities is to mirror it from within. We believe the fusion of various perspectives results in the best outcomes for our employees, our company, our customers, and the world around us. We are committed to an inclusive environment where every individual can thrive through a sense of belonging, respect and contribution.

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