Manufacturing Data Engineer

AOI; Sugar Land (TX); On-site; USD 90,000 - 130,000; Full time

Job summary

Applied Optoelectronics, Inc. (AOI) in Sugar Land, TX, seeks a Manufacturing Data Engineer to join the Process Integration team.

You will develop data infrastructure and analytic tools connecting fab process, equipment, metrology, device-test, yield, and reliability data, using Python, SQL, JMP, and statistics to automate data processing and improve traceability. You will help maintain the fab process dashboard and collaborate with Process Integration, Yield Engineering, Fab, Equipment

Qualifications

• Bachelor’s degree in a technical field
• 3+ years of data engineering or manufacturing analytics
• Strong Python for data processing, automation, analysis, and visualization
• Experience with SQL and relational databases
• Experience with JMP for statistics and scripting
• Familiarity with manufacturing statistics (SPC, DOE, regression)
• Ability to manage large manufacturing datasets
• Ability to collaborate with engineering, manufacturing and IT teams
• Familiarity with LabVIEW is a plus
• Excellent communication skills across teams

Responsibilities

• Develop Python scripts for data collection, cleaning, reduction, analysis, visualization, and automated reporting
• Build and maintain databases integrating 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 with Equipment Engineering and IT
• Develop automated alerts to identify process shifts, equipment abnormalities, and excursions
• Provide reliable datasets and analytical workflows to support SPC, DOE, and reliability analyses
• Translate analytical requirements into scalable databases, scripts, dashboards, and reports
• Establish data-validation rules and monitor data quality
• Document databases, scripts, dashboards, interfaces, and standard analysis methods

Skills

Python programming

SQL

Data engineering

Dashboard development

JMP

LabVIEW

Communication

Education

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

Tools

LabVIEW

JMP

Job description

Applied Optoelectronics, Inc. (AOI) is seeking a Manufacturing Data Engineer to join the Process Integration team at its Sugar Land facility.

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.
• Document databases, scripts, dashboards, interfaces, and standard analysis methods.

Qualifications

• 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