Site Performance Analytics Product Owner

Career Area:

Engineering

Job Description:

Your Work Shapes the World at Caterpillar Inc.

When you join Caterpillar, youre joining a global team who cares not just about the work we do – but also about each other. We are the makers, problem solvers, and future world builders who are creating stronger, more sustainable communities. We dont just talk about progress and innovation here – we make it happen, with our customers, where we work and live. Together, we are building a better world, so we can all enjoy living in it.

Role Definition

Owns the definition, validation, and operational correctness of site performance analytics including KPIs, dashboards, and insights; collaborates with data engineering and BI teams to ensure that internal and external stakeholders can accurately manage productivity, uptime, utilisation, safety, and operating cost through reliable data interpretation.

Responsibilities

• Defining and validating site-level KPIs including logic, formulas, thresholds, and assumptions aligned to operational performance.
• Ensuring KPI definitions are operationally correct and validated with Sites and Dealers prior to deployment.
• Designing and structuring site performance dashboards, including KPI placement, navigation, and user flows for multiple personas.
• Creating dashboards that enable users to identify issues, diagnose causes, and take corrective actions.
• Identifying and validating data sources including machine data, telematics, and site systems to establish a single source of truth.
• Detecting data gaps, inconsistencies, and misleading signals and driving their resolution.
• Generating operational insights from data to highlight performance loss, inefficiencies, and abnormal behaviour.
• Translating KPI logic and dashboard requirements into clear inputs for data engineering and BI teams.
• Reviewing dashboard builds to ensure correctness, alignment to site reality, and high signal-to-noise ratio.
• Engaging with Sites, Dealers, and internal teams to validate outcomes and improve analytics adoption.
  
  

• Applies knowledge of site operations, fleet performance, and machine behaviour to define relevant KPIs.
• Educates stakeholders on productivity, utilisation, uptime, and cost drivers.
• Interprets operational trends and connects them to data signals.
• Assesses the impact of operational practices on KPI accuracy.
• Promotes best practices in fleet monitoring and performance analysis.
• Explains operational cycles and their influence on performance metrics.
  
  

• Diagnoses root causes of performance issues using data and operational context.
• Identifies inconsistencies in analytics and drives corrective action.
• Applies structured problem-solving approaches across multiple data sources.
• Synthesises information to determine accurate operational insights.
• Develops solutions to improve KPI accuracy and data reliability.
• Resolves critical issues impacting decision-making and operations.
  
  

• Converts KPI definitions into structured requirements for delivery teams.
• Understands data flows supporting analytics platforms.
• Participates in validation of implemented analytics solutions.
• Reviews outputs to ensure alignment with defined logic.
• Supports iterative enhancement based on feedback.
• Ensures solutions meet business and operational needs.
  
  

• Validates dashboard outputs against actual site conditions.
• Ensures KPI calculations are consistent across scenarios.
• Identifies deviations between expected and actual results.
• Supports testing for accuracy and usability of analytics solutions.
• Verifies that dashboards enable correct decision-making.
• Contributes to validation prior to stakeholder deployment.
  
  

• Uses BI tools such as Power BI to review and validate dashboards.
• Understands strengths and limitations of analytics platforms.
• Works with engineering teams on implementation improvements.
• Assesses effectiveness of tools in delivering insights.
• Supports enhancements to analytics tools and dashboards.
• Ensures tools accurately represent operational data.
  
  

• Interprets advanced analytics outputs for operational use.
• Supports application of predictive or anomaly-based insights.
• Assists in aligning analytics outputs to business outcomes.
• Validates advanced analytics against real-world behaviour.
• Uses data patterns to identify early performance issues.
• Ensures analytics outputs are practical and actionable.
  
  

• Understands logic behind KPI calculations and data transformations.
• Interprets implementation of analytics and data pipelines.
• Supports debugging of KPI or dashboard issues.
• Reviews logic implementation for correctness.
• Collaborates with developers to refine solutions.
• Ensures alignment between design and implementation.
  
  

• Identifies root causes of inaccurate analytics or dashboards.
• Resolves issues across data, logic, and visualisation layers.
• Drives correction of data inconsistencies affecting outcomes.
• Ensures reliability of analytics in operational environments.
• Coordinates resolution across technical and data teams.
• Prevents recurrence through validation and governance practices.