Senior Product Development Test Engineer, Data Analytics

Job Title

Senior Product Development Test Engineer, Data Analytics

Role Summary

Senior data engineer responsible for designing and delivering end-to-end data solutions for semiconductor test engineering data (wafer sort, final test, parametric, STDF). Works with test engineering, product engineering, yield and NPI teams to translate analytics requirements into scalable cloud ETL and data architectures and to productionize pipelines with IT.

Experience Level

Senior — typically 5–10 years in Data Engineering / Platform Engineering or equivalent experience working on large-scale ETL, data pipelines and analytics platforms.

Responsibilities

Deliver scalable data ingestion, transformation and serving layers tailored to high-volume semiconductor test data and engineering analytics.

• Design and implement end-to-end ETL/ELT pipelines (ingestion → transformation → modeling → consumption) for large-scale test data.
• Develop business logic and data transformations for workflows such as hard/soft binning, yield analysis, and parametric trend evaluation.
• Rapidly prototype solutions to support analytics, yield improvement and test program optimization.
• Translate business needs into maintainable data architectures supporting high-volume, high-velocity ingestion and processing.
• Partner with IT to productionize pipelines with reliability, monitoring, observability and governance.
• Improve pipeline performance and apply cloud ETL best practices for distributed processing of STDF/ATE data.
• Ensure data quality via validation, reconciliation and cross-stage traceability (wafer → package → final test).
• Support data modeling and performance improvements for dashboards, failure analysis and engineering workflows.
• Enable data lineage and traceability to support root cause analysis and debugging.
• Drive reusable patterns and frameworks for test data ingestion, normalization and standardization across suppliers.

Requirements

Key technical skills and platform experience required and preferred. Degree requirements are listed separately in Education Requirements below.

Must-have

• 5–10 years in Data Engineering / Platform Engineering or equivalent experience.
• Experience with cloud data platforms — AWS required.
• Strong Python experience (PySpark, Pandas, ETL frameworks).
• Proficient in SQL, data modeling and performance tuning.
• Experience designing end-to-end data solutions and understanding the full data lifecycle (ingestion → transformation → serving).
• Experience with Databricks, Snowflake or Spark-based systems.

Nice-to-have / Preferred

• Experience with Data Mesh / domain data-product architectures.
• Familiarity with metadata/catalog platforms (Data Catalog, Glue Catalog, Unity Catalog).
• Experience with RAG / AI data pipelines, vector stores or agentic AI patterns.
• Familiarity with API-based data access patterns and MCP-style integrations.
• Experience with semiconductor or manufacturing test data (STDF, parametric, yield analysis).
• AWS Certified Solutions Architect – Professional (preferred).

Education Requirements

Minimum education options: Bachelor's degree in Engineering, Information Systems, Computer Science, Electrical/Electronics Engineering or related field (with relevant experience); or Master's degree in those fields with reduced experience requirement; PhD also cited as an option. Listings also allow equivalent related work experience in lieu of degree. AWS certification (Solutions Architect – Professional) is listed as a preferred certification.

About the Company

Company: Qualcomm

Headquarters: San Diego, California, United States

Qualcomm is a global leader in semiconductor and telecommunications equipment, specializing in mobile technologies and innovations. Known for its Adreno GPUs, the company provides solutions enabling advancements in mobile gaming, AI, VR/AR, and autonomous driving. Qualcomm's cutting-edge technology and commitment to high-performance, power-efficient designs drive the evolution of mobile graphics and connectivity worldwide.