Entegris

Lead, Manufacturing Performance Analytics

Entegris$120K — $149K *
Manufacturing & Automotive
8 - 10 years of experience
Job Overview by Ladders

Qualifications

  • Bachelors of Science in Engineering, Data Science, or similar
  • 10+ years of experience in operations/manufacturing data support roles
  • Proficient in data manipulation and visualization using Power BI, Tableau, or similar tools
  • Understanding of Statistical Process Control (SPC) in manufacturing is a plus
  • Strong collaborative skills with cross-functional stakeholders at all levels
  • Tenacity in translating broad requirements into specific functionality
  • Excellent presentation skills for technical data

Responsibilities

  • Develop technical expertise in data architecture across business systems, particularly ERP and SPC software
  • Contextualize data with knowledge of multi-site processes and operations
  • Translate business questions into actionable analytics through collaboration with key teams
  • Drive consistent metric definitions and promote visual management tools for performance monitoring
  • Build Daily Operations Cockpits to automate relevant dashboards for operational functions
  • Integrate new data sets into Google Cloud Platform for enhanced analytics
  • Construct predictive analysis dashboards to inform business decisions and solve issues

Benefits

  • Opportunity to work in a dynamic environment with a collaborative company culture
  • Support for professional development and cross-functional networking
  • Access to cutting-edge data visualization tools and technologies
  • Involvement in impactful projects with global reach
  • Potential for professional growth in a rapidly evolving industry
Full Job Description

Job Title:

Lead, Manufacturing Performance Analytics

Job Description:

THE ROLE

Entegris is seeking a highly motivatedManufacturing Performance AnalyticsLeadto support our Advanced Purity Solutions division. The Manufacturing Performance Analytics Lead is accountable for enabling data driven operational decision making by delivering standardized, scalable analytics that provide clear visibility to manufacturing performance across sites.

This role defines and owns the core execution metrics, dashboards, and analytic views spanning capacity, throughput, WIP, cycle time, yield, and on time shipment, ensuring alignment to standardized master data and execution processes.

WHAT YOU 27LL DO

  • Develop strong technical knowledge of data architecture across business systems, especially ERP, SPC software, LIMS, and cloud data bases.

  • Gain working knowledge of processes and operations across multiple sites and business units to contextualize data to better deliver impactful data visualizations to site ops teams.

  • Partner closely with Operations, Engineering, Planning, and Digital teams to translate business questions into actionable analytics.

  • Ensure consistent metric definitions and targets by functional area, and drive adoption of visual management tools that allow factories to identify constraints, prioritize actions, and proactively manage performance at the shopfloor, site, and network levels.

  • Collaborate with local and global stakeholders to build out the Daily Operations Cockpits 6 a series of dashboards that are job function aligned to provide automated and consistent data relevant to the daily work for key functions within the operations organization.

  • Identify opportunities to bring new data sets into the Google Cloud Platform data lake to be used inthe OperationsCockpits, process controls, and predictive analyses applications.

  • Construct predictive analysis data dashboards pulling together and transforming data sets relating raw material, production process, and finished good data to solve technical problems and/or inform business decisions.

  • Maintain global scalability of dashboards and data structures built to enable translation to other sites and business units.

  • Build and maintain relationships across the global organization to enable effective leadership and collaboration, especially networking with other members of the globalopsorganization working on data visualization projects.

  • Communicate effectively across the organization (BU, Operations, NPI, NPD, R&D, etc.) to drive results. Contribute to the direction of the department in partnership with other members of the team and leadership.

  • Ability to wear PPE as necessary for work in a cleanroom andlift upto 50lbs

WHAT WE SEEK

  • Bachelors of Sciencein Engineering, Data Science, or similar

  • 10+ years of experience in operations/manufacturing data support roles

  • Data manipulation and visualization skills using Power BI, Tableau, or similar

  • Understanding of Statistical Process Control (SPC) and its application in manufacturing a plus

  • Desire to collaborate with varied cross functional stake holders at all levels

  • Tenacity to drive projects to completion translating broad requirements into specific functionality or visuals to meet business needs

  • Ability to maintain positive/collaborative working relationships with cross functional stakeholders at all levels of the organization

  • Ability to present technical data to all levels of the organization

  • Ability to travel domestically and/or internationally in support of Global Process Engineering team charter (up to 20%)

Your success will be measured by:

  • Standardized Manufacturing Performance Visibility

  • Core execution metrics (capacity, throughput, WIP, cycle time, yield, and on-time shipment) are clearly defined, documented, and consistently applied across supported sites.

  • Dashboards provide accurate, timely, and trusted visibility into manufacturing performance at shop floor, site, and network levels, aligned to standardized master data and execution processes.

  • Actionable Analytics Adoption

  • Operations, Engineering, Planning, and other stakeholders actively use analytics outputs to support daily tactical decision-making, as well as strategiclong rangeplanning.

  • Visual management tools, including Daily Operations Cockpits, are embedded in routine operational reviews and enable proactive performance management.

  • Scalable and Sustainable Analytics Solutions

  • Dashboards and data structures are designed for global scalability and effective translation to additional sites and business units.

  • New and existing data sets are integrated into the Google Cloud Platform data lake to support analytics, process controls, and predictive analysis applications

  • Predictive and Problem-Solving Impact

  • Predictive analytics combine raw material, process, and finished goods data to inform business decisions and address technical challenges.

  • Analytics outputs support problem identification, root cause analysis, and operational performance improvement initiatives.

  • Cross-Functional Collaboration and Influence

  • Strong working relationships are established across Operations, Engineering, Planning, Digital, and global stakeholders to translate business questions into actionable analytics.

  • Technical data and insights are communicated effectively at all organizational levels to drive alignment and informed decision-making

  • Projects are driven to completion with accountability, professionalism, and adherence to Entegris PACE values.

About Entegris

Entegris is a global leader in materials integrity management. The company designs, manufactures, and supplies a range of products and materials used in advanced manufacturing processes in the semiconductor and other high-tech industries. Entegris' products are used in the production of microelectronics, flat panel displays, data storage devices, and other high-tech products. The company's products include wafer shippers, wafer transport and handling systems, chemical delivery systems, and filtration systems. Entegris was founded in 1966 and is headquartered in Billerica, Massachusetts.
Learn more about Entegris
Size
6,850 employees
Market Cap
$9.4 billion
Industry
Net Income
$294.9 million
Founded
1966
5 Year Trend
+14.4%
Revenue
$1.8 billion
NASDAQ

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