Data Manufacturing Engineer

This position will develop and maintain the data infrastructure and analytical tools used to connect fab process, equipment, metrology, device-test, yield, and reliability data. The engineer will use Python, SQL, JMP, and statistical methods to automate data processing, improve manufacturing traceability, and provide reliable analytical tools for engineering and production teams.

The engineer will also help maintain and monitor AOI’s existing fab process dashboard and work closely with Process Integration, Yield Engineering, Fab, Equipment Engineering, Quality, Reliability, and IT teams.

Job Duties

Develop Python scripts for data collection, cleaning, reduction, analysis, visualization, and automated reporting.

Build and maintain databases that integrate fab process, equipment, metrology, device-test, yield, and reliability data.

Establish data traceability across product, lot, wafer, process step, tool, recipe, operator, and timestamp.

Develop automated JMP workflows, scripts, reports, and visualization tools for manufacturing-data analysis.

Create and maintain dashboards, wafer maps, trend charts, and automated reports for engineering and production teams.

Maintain and monitor the existing fab process dashboard.

Troubleshoot dashboard, data-connection, and data-integrity issues in collaboration with Equipment Engineering and IT.

Develop automated alerts that help process and yield engineers identify process shifts, equipment abnormalities, and manufacturing excursions.

Provide reliable datasets and analytical workflows to support SPC, process capability, DOE, correlation, reliability, and root-cause analyses.

Work with yield engineers and process owners to translate analytical requirements into scalable databases, scripts, dashboards, and reports.

Establish data-validation rules and monitor the accuracy, completeness, and consistency of manufacturing data.

Bachelor’s degree in Data Science, Computer Science, Engineering, Statistics, Physics, Mathematics, or a related technical field.

3 or more years of experience in data engineering, manufacturing analytics, database development, scientific software, or equipment-data automation.

Strong Python programming skills, particularly for data processing, automation, analysis, and visualization.

Strong working knowledge of JMP for statistical analysis, visualization, and preferably JMP Scripting Language.

Experience with SQL, relational databases, database design, and combining data from multiple sources.

Working knowledge of manufacturing statistics, including SPC, process capability, regression, ANOVA, DOE, and measurement-system analysis.

Ability to clean, reduce, analyze, and manage large manufacturing datasets.

Familiarity with LabVIEW and the ability to maintain and troubleshoot an existing LabVIEW-based dashboard.

Ability to communicate effectively with engineering, manufacturing, quality, reliability, and IT teams.

Preferred

Master’s degree in Data Science, Computer Science, Engineering, Statistics, or a related technical field.

Experience in semiconductor fabrication, optoelectronics, photonics, or another high-volume manufacturing environment.

Experience with Python libraries such as pandas, NumPy, SciPy, matplotlib, seaborn, or Plotly.

Experience analyzing wafer maps, equipment histories, metrology results, product-test data, reliability data, or manufacturing yield.

Experience developing automated reports, process-monitoring alerts, and interactive dashboards.

Familiarity with manufacturing execution systems, equipment databases, SPC systems, or quality-management systems.

Experience with REST APIs, Git, Linux, or software version-control practices.

Key Competencies

Strong Python, SQL, JMP, and data-management capability.

Good understanding of database structure and manufacturing-data traceability.

Working knowledge of manufacturing statistics and data visualization.

Strong data-quality and troubleshooting skills.

Ability to understand manufacturing requirements and convert them into practical data solutions.

Effective cross-functional collaboration.

Clear technical communication and documentation.