Data Engineer, Staff (Semiconductor Engineering)

Qualcomm; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Qualcomm Global Trading Pte. Ltd. seeks a Staff Data Engineer to design scalable data platforms, govern data products, and enable advanced analytics for semiconductor engineering insights.

You will partner with cross-functional teams across Test Engineering, Product Engineering, Yield, and NPI to transform complex semiconductor test data into reusable data assets and analytics solutions, building cloud-based pipelines and productionizing them with strong governance and reliability.

Qualifications

• 8-15 years of experience in Data Engineering or related fields.
• Experience with cloud data platforms, preferably AWS.
• Proficient in Python (PySpark, Pandas) and SQL.
• Experience delivering end-to-end data solutions across ingestion to analytics.
• Hands-on with Databricks, Snowflake, Spark or equivalent.
• Strong knowledge of data governance and data product principles.
• Ability to collaborate across business, engineering and IT.

Responsibilities

• Design, develop, and optimize end-to-end data products and ETL/ELT solutions for large datasets.
• Translate business requirements into scalable cloud architectures supporting high-volume data.
• Lead governance, metadata, lineage, and data quality practices for data assets.
• Collaborate with Test Engineering, Product Engineering, Yield, and NPI to productionize solutions.

Skills

Cloud data platforms AWS

Python PySpark Pandas

SQL data modeling

Databricks Snowflake Spark

Data Governance

Data Product knowledge

Cross-functional collaboration

Education

Bachelor's degree in Engineering/CS/IS

Master's degree in Engineering/CS/IS

PhD in Engineering/CS (optional)

Tools

Databricks

Snowflake

Spark

ETL frameworks

Job description

Company:

Qualcomm Global Trading Pte. Ltd.

Job Area:

Engineering Group, Engineering Group > Software Engineering

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.
• 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

• Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 4+ years of Software Engineering or related work experience.
• Master's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Software Engineering or related work experience.
• 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.

Applicants: Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail [e-mail] or call Qualcomm's toll-free number found here. Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries).

Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.

If you would like more information about this role, please contact Qualcomm Careers.