Staff Data Engineer — Cloud Data for Semiconductors

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

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.