Data Engineer

About the Role

Key Responsibilities

• Design, build, and deploy data-driven solutions to support analytics, automation, and process improvements.
• Partner with quality engineers, factory teams, and stakeholders to develop and operationalize data systems.
• Establish scalable data flows, architectures, and software components for data ingestion, processing, and management.
• Write robust, maintainable Python code to create reusable libraries, APIs, and automation tools.
• Apply AI and machine learning techniques for root cause analysis, predictive modeling, and smart data access.
• Implement best practices for data quality, integrity, and governance across TI's infrastructure.
• Continuously evaluate and improve data systems and business processes based on technical and user feedback.

Requirements

• Bachelor's degree in Electrical Engineering, Computer Engineering, or a related technical field.
• Minimum of 5 years of relevant professional experience in data engineering, software development, or business process automation.
• Minimum of 5 years of Semiconductor or related technology experience.
• Proficiency in Python scripting, including building reusable functions and libraries.
• Hands-on experience working with SQL and relational databases to retrieve, transform, and manage data.
• Experience developing tools or solutions that support business process automation, data analytics, or customer quality workflows.

Nice to Have

• Master's degree in a related engineering or data-focused field.
• Experience supporting semiconductor quality or manufacturing operations.
• Familiarity with machine learning, AI integration, and large language models (LLMs).
• Experience building internal tools or dashboards using platforms like Streamlit, Power BI, or equivalent.
• Knowledge of TI's internal data systems or previous collaboration with TI quality or factory teams.
• Strong understanding of data architecture, ETL pipelines, and principles of data integrity and scalability.
• Experience working with vector databases and implementing Retrieval-Augmented Generation (RAG) pipelines.