2 months ago
Singapore, SingaporeStaff+
Responsibilities
- Design, develop, and optimize scalable data products and ETL/ELT solutions for large-scale semiconductor engineering datasets.
- Build cloud-based ingestion, transformation, semantic modeling, serving, and analytics solutions across the data lifecycle.
- Implement business logic for yield analysis, hard and soft binning, parametric trend monitoring, failure analysis, and test program optimization.
- Prototype and productionize reliable, observable, governed enterprise data platforms with domain experts, data owners, and IT teams.
- Establish data governance and stewardship practices including business glossaries, semantic definitions, data dictionaries, metadata, lineage, data quality, access management, and compliance controls.
- Improve platform scalability, performance, reliability, and traceability for large-scale STDF/ATE data processing across wafer, package, and final test stages.
Requirements
- 8-15 years of experience in Data Engineering, Data Platform Engineering, or related disciplines.
- Bachelor's degree in Engineering, Information Systems, Computer Science, or a related field with 5-10 years of Data Engineering or ETL Development experience, or a master's degree with 3+ years of related experience.
- Bachelor's degree in Engineering, Information Systems, Computer Science, or a related field with 4+ years of Software Engineering experience, or a master's degree with 3+ years, or a PhD with 2+ years.
- At least 2 years of experience with a programming language such as C, C++, Java, or Python.
- Hands-on experience with cloud data platforms, preferably AWS.
- Proficiency in Python, PySpark, Pandas, ETL frameworks, and SQL, including data modeling, optimization, and performance tuning.
- Experience delivering end-to-end data solutions from ingestion and transformation through serving and analytics consumption.
- 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, stewardship, metadata management, data quality management, data products, ownership models, security controls, and enterprise access management.
- Experience creating data product documentation, business glossaries, data dictionaries, lineage artifacts, and governance standards.
- Ability to collaborate across business, engineering, and IT organizations to deliver scalable, governed, production-ready solutions.
- Preferred qualifications include exposure to MCP/API-based data access patterns, semiconductor or manufacturing data environments, STDF, parametric test data, yield analysis, and AWS Certified Solution Architect - Professional.
About Qualcomm
Qualcomm is a public semiconductor company headquartered in San Diego, founded in 1985, that designs and sells wireless chipsets and platforms for mobile devices, automotive, IoT, and networking, notably the Snapdragon application processors and 5G modems. It also licenses a large portfolio of cellular patents to device makers, generating revenue alongside chip sales; its technology underpins many Android smartphones and emerging automotive and edge-compute systems, and it trades on NASDAQ as QCOM.