Lead Engineer, Manufacturing Engineering & Data Systems

Entegris•Hillsboro, OR

About The Position

Entegris is seeking a Lead Engineer, Manufacturing Engineering and Data Systems for its Hillsboro, Oregon site. The role supports the Hillsboro CMP Polishing Pads factory through data analytics, reporting, automation, and digital transformation. It works with Manufacturing, Production Operations, Quality, Supply Chain, Planning, and IT to turn data into actionable insights that improve operational performance, product quality, and business decision-making. The position reports to the Director of Operations. The Lead Engineer develops analytical tools and data solutions, supports manufacturing and engineering teams with analysis, and communicates findings to technical and non-technical audiences. The role also contributes to equipment and process improvements, continuous improvement initiatives, site-wide data governance, and the transfer of best practices across a global engineering team.

Requirements

Bachelor’s or master’s degree in Mechanical Engineering, Chemical Engineering, Materials Science and Engineering, Statistics, Data Analytics, Computer Science, or a related field; equivalent experience is considered.
At least 8 years of experience in data analytics, business intelligence, manufacturing analytics, or operational analytics.
Strong experience developing dashboards and reports using PowerBI.
Experience developing automated solutions using JMP, Python, or other software.
Advanced SQL skills and ability to work with large, complex datasets.
Hands-on experience with cloud data platforms such as GCP or Databricks.
Ability to design and communicate data models, views, and reporting datasets.
Strong understanding of data quality concepts, root cause analysis, and data validation techniques.
Ability to troubleshoot data pipeline and data integration issues while partnering with IT teams.
Experience supporting manufacturing or semiconductor facilities.
Strong communication skills and ability to bridge technical and functional teams.
Self-motivated and customer-focused, with the ability to work independently while leading cross-functional initiatives.

Nice To Haves

BSE with 8 years, MSE with 5 years, or PhD; the posting describes these as preferred while also allowing equivalent experience.
Capital project management experience.
Six Sigma Green Belt or Black Belt.

Responsibilities

Develop and maintain automated analytical tools, dashboards, and reporting solutions using PowerBI, JMP, SQL, Ignition, MES, LIMs, and other analytical technologies.
Provide ad-hoc and one-time analytical support to manufacturing, engineering, production operations, and other factory teams.
Analyze production, quality, and business datasets to identify trends, opportunities, risks, and root causes.
Build interactive dashboards and KPI scorecards to show operational performance and support data-driven decision-making.
Design, develop, and maintain data models, views, and datasets in Google Cloud Platform, Snowflake, and related cloud-based data environments.
Collaborate with IT and data engineering teams to develop scalable solutions and improve data accessibility.
Investigate, troubleshoot, and resolve data quality issues by identifying root causes and coordinating corrective actions with IT and business teams.
Support implementation and adoption of analytical tools through training, documentation, and knowledge sharing.
Communicate analytical findings and recommendations through presentations, reports, and dashboards.
Promote data governance, data quality standards, and best practices across the site.
Stay current with analytics, AI, automation, and visualization techniques and evaluate potential business applications.
Participate in a global engineering team and transfer best practices across sites and unit operations.
Improve quality and utilization of automation equipment and processes, and develop plans to expand future equipment using current learnings.
Work with cross-functional teams on continuous improvements to manufacturing profitability, efficiency, and productivity.