A degree in engineering, science, data, computing, or a related subject, or equivalent industry experience.
Advanced hands-on experience with business intelligence tools such as Power BI, including data modelling, DAX measures, dashboard design and publishing, and managing refresh, security and workspaces.
Advanced Power Query (M) skills, including connecting to and combining multiple sources and cleaning and transforming large datasets.
Ability to turn data into clear messages for engineers and senior managers.
Experience with generative AI tools and agents.
Data engineering experience with pipelines, data warehouses, or cloud platforms.
Responsibilities
Build and maintain dashboards and reports for manufacturing performance, including cycle time, OEE, yield, WIP, capacity, and quality.
Build data models that turn data from manufacturing and business systems, such as MES, FDC, SPC, ERP, scheduling, and equipment data, into meaningful information using databases provided and managed by IT.
Validate data used in reporting, agree KPI definitions, and raise and follow up data issues with IT and system owners.
Build dashboards for the enterprise KPI framework, showing actual performance against target for every function.
Automate management reporting, including monthly business reviews and strategy deployment (Hoshin Kanri) dashboards.
Maintain reporting standards and KPI definitions, and support IT with data governance.
Own and develop equipment and process monitoring systems, such as FDC, so engineers receive useful alerts and insights.
Apply statistical analysis and predictive analytics to find the causes of losses and variation.
Identify and deliver automation of manual reporting and routine workflows.
Provide data to support capacity and simulation models used for planning and investment decisions.
Measure the financial and operational benefits of improvement projects for Finance sign-off.
Lead data analysis in improvement projects and performance reviews, and coach cross-functional teams to use data effectively.
Desired Qualifications
Knowledge of statistical process control and design of experiments.
Proven experience analysing data in a manufacturing or high-tech environment.
Advanced SQL skills, including complex queries using joins, CTEs, and window functions; views and stored procedures; and query performance tuning on large databases.
Experience with manufacturing systems data, for example MES, FDC, SPC, or ERP.
Experience in semiconductor, electronics, or other high-tech manufacturing.
Experience with Microsoft Fabric, including lakehouse, dataflows, and pipelines.
Plessey is looking for a Data & Performance Engineer to turn manufacturing and business data into trusted information and clear dashboards that drive faster, better decisions. Reporting to the Operational Excellence & Industrial Engineering team, leading continuous improvement across the business.
Working with IT, who own the business's databases, you will turn system data into meaningful information through validation, data modelling, analytics, reporting and automation, with a focus on improving cycle time, cost, quality and productivity
Build and maintain dashboards and reports for manufacturing performance, including cycle time, OEE, yield, WIP, capacity and quality.
Build data models that turn data from manufacturing and business systems, such as MES, FDC, SPC, ERP, scheduling and equipment data, into meaningful information, using databases provided and managed by IT.
Validate data used in reporting, agree KPI definitions, and raise and follow up data issues with IT and system owners.
Build dashboards for the enterprise KPI framework, showing actual against target for every function.
Automate management reporting, including monthly business reviews and strategy deployment (Hoshin Kanri) dashboards.
Maintain reporting standards and KPI definitions, and support IT with data governance.
Own and develop equipment and process monitoring systems, such as FDC, so engineers get useful alerts and insight.
Apply statistical analysis and predictive analytics to find the causes of losses and variation.
Identify and deliver automation of manual reporting and routine workflows.
Provide data to support capacity and simulation models used for planning and investment decisions.
Measure the financial and operational benefits of improvement projects for Finance sign-off.
Lead data analysis in improvement projects and performance reviews, and coach cross-functional teams to use data effectively.
Degree in engineering, science, data, computing or a related subject, or equivalent industry experience.
Advanced, hands-on experience with BI tools such as Power BI: data modelling, DAX measures, dashboard design and publishing, and managing refresh, security and workspaces.
Advanced Power Query (M): connecting to and combining multiple sources, and cleaning and transforming large datasets.
Able to turn data into clear messages for engineers and senior managers.
Experience with generative AI tools and agents.
Data engineering experience (pipelines, data warehouses or cloud platforms).
Advantage
Knowledge of statistical process control and design of experiments.
Proven experience analysing data in a manufacturing or high-tech environment.
Advanced SQL: complex queries (joins, CTEs, window functions), views and stored procedures, and query performance tuning on large database.
Experience with manufacturing systems data, for example MES, FDC, SPC or ERP.
Experience in semiconductor, electronics or other high-tech manufacturing.
Experience with Microsoft Fabric (lakehouse, dataflows, pipelines).