Data Engineer, Staff (Semiconductor Engineering)

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General Summary:

Role Summary

• Serve as a Staff Data Engineer leading the design and implementation of scalable data platforms, governed data products, and advanced analytics capabilities that drive semiconductor engineering insights.

• Partner with cross-functional teams across Test Engineering, Product Engineering, Yield, and NPI to transform complex semiconductor test data into reusable, high-quality data products and analytics solutions. Leverage cloud-native technologies to develop business logic, data pipelines, and rapid prototypes, while working closely with IT to transition solutions into production environments with strong standards for scalability, governance, reliability, and operational support.

• This role plays a key part in enabling timely, trusted, and actionable insights from high-volume semiconductor data sources, improving engineering productivity, decision-making, and overall solution effectiveness.

Key Responsibilities

• Design, develop, and optimize end-to-end data products and ETL/ELT solutions for large-scale semiconductor engineering datasets, including wafer sort, final test, parametric, quality, and manufacturing data. Translate business requirements into scalable cloud-based architectures that support high-volume, high-velocity data ingestion, transformation, semantic modeling, and analytics consumption.

• Drive engineering analytics capabilities by implementing business logic for yield analysis, binning (hard/soft bin), parametric trend monitoring, failure analysis, and test program optimization. Partner closely with domain experts, data owners, and IT teams to rapidly prototype solutions and productionize them into reliable, observable, and governed enterprise platforms.

• Establish and maintain domain data products, governance frameworks, and stewardship practices, including business glossaries, semantic definitions, data dictionaries, metadata management, lineage tracking, data quality controls, access management, and compliance requirements. Ensure trusted, discoverable, reusable, and self-service data assets that support Data Mesh principles and data-driven decision-making.

• Continuously improve platform scalability, performance, and reliability by applying cloud data engineering best practices, optimizing large-scale STDF/ATE data processing, implementing reusable ingestion and standardization frameworks, and enabling end-to-end traceability across test stages from wafer to package and final test.

Required Qualifications

• 8-15 years of experience in Data Engineering, Data Platform Engineering, or related disciplines.
• Strong hands-on experience with cloud data platforms, preferably AWS.
• Proficient in Python (PySpark, Pandas, ETL frameworks) and SQL (data modeling, optimization, and performance tuning).
• Experience designing and delivering end-to-end data solutions across the complete data lifecycle, from ingestion and transformation to serving and analytics consumption.
• Hands-on experience with modern data platforms such as Databricks, Snowflake, Spark, or equivalent cloud-native technologies.
• Strong understanding of semiconductor test data domains, including yield, quality, manufacturing, and engineering analytics.
• Knowledge of Data Governance, Data Stewardship, Metadata Management, Data Quality Management, and Data Product principles.
• Experience creating and maintaining data product documentation, business glossaries, data dictionaries, lineage artifacts, and governance standards.
• Understanding of data ownership models, governance processes, security controls, and enterprise access management practices.
• Proven ability to collaborate across business, engineering, and IT organizations to deliver scalable, governed, and production-ready data solutions.

Preferred Qualifications

• Exposure to:
• MCP / API-based data access patterns
• Experience in semiconductor / manufacturing data environments (e.g., STDF, parametric test data, yield analysis)
• AWS Certified Solution Architect - Professional

Minimum Qualifications

• Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 5-10 years of Data Engineering, ETL Development experience, or related work experience.

OR

• Master's degree in Engineering, Information Systems, Computer Science, or related field and 3+ year of Data Engineering, ETL Development experience, or related work experience.

Minimum Qualifications:

OR

Master's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Software Engineering or related work experience.

OR

PhD in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.

• 2+ years of work experience with Programming Language such as C, C++, Java, Python, etc.

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