Lead Engineer, Manufacturing Engineering & Data Systems

Entegris•Hillsboro, OR

2d•Onsite

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 closely with Manufacturing, Production Operations, Quality, Supply Chain, Planning, and IT to turn data into actionable insights that improve operational performance, product quality, and business decisions. The position reports to the Director of Operations. The engineer develops analytical solutions and supports manufacturing processes and equipment improvements. As part of a global engineering team, the role also transfers best practices across sites and unit operations and leads cross-functional initiatives to improve manufacturing profitability, efficiency, and productivity.

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.
Strong communication skills and ability to bridge technical and functional teams.
Self-motivated and customer-focused, with the ability to work independently and lead cross-functional initiatives.

Nice To Haves

Eight years of experience with a bachelor’s degree, five years with a master’s degree, or a PhD is preferred; equivalent experience is considered.
Experience supporting manufacturing or semiconductor facilities.
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 for 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 decisions.
Design, develop, and maintain data models, views, and datasets in Google Cloud Platform, Snowflake, and related cloud data environments.
Collaborate with IT and data engineering teams to develop scalable solutions and improve data accessibility.
Investigate and resolve data quality issues by identifying root causes and coordinating corrective actions with IT and business teams.
Support adoption of analytical tools through training, documentation, and knowledge sharing.
Communicate analytical findings and recommendations to technical and non-technical audiences through presentations, reports, and dashboards.
Promote data governance, data quality standards, and best practices across the site.
Monitor emerging analytics, AI, automation, and visualization techniques and evaluate opportunities to apply them to the business.
Participate in a global engineering team to transfer best practices across sites and unit operations.
Improve quality and utilization of automation equipment and processes.
Develop plans and lead expansion of future equipment, applying current learnings to enhance subsequent equipment revisions.
Work with cross-functional teams to drive continuous improvements in manufacturing profitability, efficiency, and productivity.