Industrial Engineer - Foundry Quality and Reliability Labs

Intel•Hillsboro, OR

1d•Onsite

About The Position

As a labs Industrial Engineer, you will play a crucial role in optimizing factory operations, improving workflow efficiency, and driving productivity to meet customer demands for lab testing and failure analysis services. This role supports Demand and Capacity Planning for lab operations while driving continuous operational improvement through the development and management of key performance indicators, and automation systems. The ideal candidate combines strong industrial engineering fundamentals with advanced data scripting capabilities and lab/manufacturing systems expertise to deliver data-informed decisions, streamlined workflows, and measurable performance gains across a dynamic foundry lab ecosystem. This role is part of Intel's Foundry Quality and Reliability Labs organization, a team dedicated to ensuring Intel's global lab capabilities meet the evolving needs of customers and the business. The group focuses on operational excellence, efficiency, and innovation in lab processes, supporting Intel's foundry mission to deliver world-class technology solutions.

Requirements

Bachelor's degree in industrial engineering, Manufacturing Engineering, or a related field with 6+ years of hands-on experience in industrial engineering or manufacturing operations OR a master's degree in industrial engineering or related field with 5+ years of hands-on experience.
Proficiency in capacity planning and factory operational planning.
Experience with lean manufacturing principles and practices.
Experience with at least one of these tools: Python, SQL / SQL PF, HTML, Power BI: Proficient in writing queries for data extraction, transformation, aggregation, and reporting from relational databases; building interactive operational dashboards.
Expertise in data analysis and modeling tools for manufacturing or lab operations optimization.
Proven ability to manage projects effectively and deliver results within deadlines.

Nice To Haves

Strong communication and collaboration skills to engage with cross-functional teams and stakeholders.
Demonstrated ability to lead initiatives that drive efficiency and innovation.
Experience with facility and equipment design to support flexible manufacturing solutions.
Familiarity with tools, processes, and metrics related to factory layout and workflow optimization.
Hands-on experience with Equipment Management Systems (EMS) for asset tracking. Ability to configure, maintain, and extract data from EMS platforms.
Working knowledge of and experience operating within ISO 9001 / 172025 Quality Management System requirements including document control, internal auditing, and management review processes.

Responsibilities

Develop, maintain, and optimize end-to-end Demand and Capacity Plans for Quality and Reliability Lab operations, including failure analysis, material analysis and reliability validation workflows.
Build and manage capacity models using industrial engineering methodologies (time studies, standard work, utilization analysis) to identify bottlenecks, over/under-capacity conditions, and optimization opportunities.
Design, develop, pilot and implement a comprehensive Key Performance Indicator (KPI) framework for Quality and Reliability Lab operations encompassing throughput time (TPT), utilization, quality yield, cost per test, and equipment effectiveness.
Build automated KPI dashboards and reporting pipelines using Power BI, Python, and SQL to provide real-time and trend-based visibility into lab performance for all stakeholder levels. Integrate KPI data streams from MES, Workstream, and IT platforms to create unified operational visibility and eliminate manual reporting redundancies.
Support the design, implementation, and continuous improvement of Quality Management System (QMS) processes within the Reliability and Quality Labs in full compliance with ISO 9001 and ISO/IEC 17025 standards.
Collaborate with cross-functional stakeholders to plan equipment purchases, layout changes, and workflow improvements.
Utilize data analysis and complex modeling techniques to enhance lab output and optimize process flows.
Define and manage tool metrics, monitor tool performance, and drive productivity improvements.