Company:

Qualcomm Global Trading Pte. Ltd.

Job Area:

Engineering Group, Engineering Group > Hardware Engineering

General Summary:

Role Summary

We are seeking a Senior Data Engineer to design and deliver end-to-end data solutions that support domain-driven analytics and fast-evolving business requirements within semiconductor test engineering environments.

This role focuses on business logic development, cloud-based ETL design, and rapid prototyping, working closely with domain teams (e.g., Test Engineering, Product Engineering, Yield, and NPI teams) to translate complex test data requirements into scalable solutions. You will also partner with IT to transition prototypes into production-grade pipelines, ensuring maintainability, scalability, and governance.

The role is critical in strengthening our ability to deliver complete, high-quality data solutions, especially for high-volume semiconductor test data (e.g., STDF, parametric, wafer sort, final test, and reliability data), while improving turnaround time and solution effectiveness.

Key Responsibilities

Design and implement end-to-end ETL/ELT pipelines (ingestion → transformation → modeling → consumption) for large-scale semiconductor test data (wafer sort, final test, and parametric datasets)

Develop business logic and data transformations aligned with test engineering workflows, including binning (hard/soft bin), yield analysis, and parametric trend evaluation

Rapidly prototype data solutions to support evolving analytics, yield improvement initiatives, and test program optimization use cases

Translate business needs into scalable and maintainable data architectures that support high-volume, high-velocity test data ingestion and processing

Partner with IT to productionize pipelines, ensuring reliability, monitoring, observability, and governance for mission-critical test data systems

Improve existing pipelines by applying cloud ETL best practices, with a focus on performance optimization for large STDF/ATE data and distributed processing

Ensure data quality via validation, reconciliation, and consistency checks, including test data integrity, bin definition alignment, and cross-stage traceability (wafer → package → final test)

Support domain teams with data modeling, usability, and performance improvements tailored for engineering analytics, yield dashboards, and failure analysis workflows

Enable data lineage and traceability across test stages, supporting root cause analysis and engineering debug

Drive reusable patterns and frameworks for faster solution delivery, particularly for test data ingestion, normalization, and standardization across suppliers (eg. OSATs, foundries)

Required Qualifications

5–10 years in Data Engineering / Platform Engineering

Strong experience in cloud data platforms (AWS required)

Hands-on expertise in:

Python (PySpark, Pandas, ETL frameworks)

SQL (data modeling, performance tuning)

Experience with:

Experience designing end-to-end data solutions, not just individual components

Strong understanding of data lifecycle (ingestion → transformation → serving)

Experience working with platforms such as Databricks / Snowflake / Spark-based systems

Preferred Qualifications

Experience with Data Mesh / Domain Data Product architecture

Familiarity with:

Metadata platforms (Data Catalog, Glue Catalog, Unity Catalog)

RAG / AI data pipelines / vector stores

Exposure to:

Agentic AI architecture (skills, tools, API-based consumption)

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:

• Bachelor's degree in Computer Science, Electrical/Electronics Engineering, Engineering, or related field and 2+ years of Hardware Engineering or related work experience.
OR
Master's degree in Computer Science, Electrical/Electronics Engineering, Engineering, or related field and 1+ year of Hardware Engineering or related work experience.
OR
PhD in Computer Science, Electrical/Electronics Engineering, Engineering, or related field.

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