Quality Data & Analytics Engineer

Corning•Village of Fairport, NY

17d•Onsite

About The Position

The Division Quality Data & Analytics Engineer bridges industrial engineering, manufacturing, and data systems. Unlike traditional IT roles, this position requires hands-on manufacturing experience and technical skills to turn fragmented data into actionable insights that improve quality and operations. The ideal candidate possesses extensive hands-on manufacturing expertise, paired with an industrial engineering perspective. They are highly skilled in Power BI, PowerApps, and the Microsoft Power Platform, enabling them to create and manage interactive dashboards, automated workflows, and advanced analytics tailored specifically for manufacturing settings. Additionally, this engineer should be able to see beyond current data sources—spotting areas where information may be missing or incomplete—and design new data pipelines, collection strategies, and integrations from scratch.

Requirements

Manufacturing fluency — understanding of manufacturing processes, quality systems, and operational workflows, able to translate floor-level needs into structured data requirements.
Power BI proficiency — advanced skill in developing interactive, multi-source dashboards and reports that communicate complex manufacturing data clearly to diverse audiences.
PowerApps and Power Automate — experienced in building workflow automation and custom applications that support quality and operations teams.
Data source architecture and pipeline development — ability to identify missing data, design new collection structures, and build integrations that bring previously uncaptured information into analytics platforms.
Systems integration knowledge — skilled in connecting and harmonizing data streams from ERP, MES, QMS, and shop floor data collection systems into unified, reliable reporting environments.
Data management — skilled in collecting, cleaning, organizing, and maintaining accurate large-scale manufacturing data sets with high attention to data integrity.
Database systems and data architecture — knowledgeable in relational database concepts, data modeling, and architecture principles that support scalable analytics solutions.
AI and emerging technology adoption — actively utilizes AI tools and new technologies to innovate data workflows, improve accuracy, and solve complex manufacturing and quality challenges.
Project leadership — experienced in managing data and systems projects independently, setting priorities, coordinating cross-functional stakeholders, and delivering results on time.
Cross-functional collaboration — highly effective working partner for process engineering, quality, operations, and IT teams — able to speak the language of the floor and the language of data with equal credibility.

Nice To Haves

Lean / Six Sigma Green Belt or Black Belt — strongly preferred; the ability to apply structured problem-solving and variation reduction methodologies directly enhances the value of this role.
SQL experience — ability to write and modify queries for data extraction, transformation, and validation across relational databases.
Industrial engineering tools and methods — familiarity with time studies, process mapping, OEE frameworks, capacity analysis, or similar IE methodologies as they relate to data collection and analysis.
Experience with shop floor data collection systems — historian platforms, SCADA, or similar real-time manufacturing data sources.
Python or R — basic scripting for data manipulation or automation is a plus.

Responsibilities

Design and manage Power Platform solutions—including Power BI dashboards, PowerApps, and automated workflows—to support QMS and PEX operations. Translate complex manufacturing data into clear visualizations for all stakeholders using SalesForce, ETQ, SAP, and MES.
Identify, assess, and close data gaps — proactively evaluate existing data sources across the quality, identify where critical data is missing, poorly structured, or uncaptured, and engineer new data collection methods, pipelines, and integrations to fill those gaps.
Use an industrial engineering perspective in data analysis by drawing on expertise in manufacturing processes, process control, OEE, yield analysis, cycle time, and reducing variation. This approach helps ensure that analytics results are practical and lead to real improvements in operations.
Conduct comprehensive data analyses supporting continuous improvement initiatives, collaborating with process engineering and quality teams to identify root causes, track performance trends, and validate improvement outcomes.
Facilitate the integration of quality and production processes within ERP and MES systems to enhance system functionality, streamline data workflows, and improve overall manufacturing performance.
Champion the adoption and adherence to QMS and CI best practices across sites, serving as a subject matter expert (SME) for data-driven decision-making, process optimization, and analytical best practices.
Leverage artificial intelligence (AI) and emerging technologies to innovate, solve complex quality and operational challenges, and continuously advance data accuracy and efficiency across the division.
Lead projects for implementing new QMS, analytics, and continuous improvement software, ensuring seamless integration with existing systems and alignment with evolving business needs — from requirements gathering through go-live and sustainment.
Proactively identify and resolve application-related issues (break/fix), maintaining the reliability, accuracy, and effectiveness of quality and manufacturing data systems across all supported sites.