Lead Business Intelligence Analyst

Career Area: Technology, Digital and Data Job Description: Your Work Shapes the World at Caterpillar Inc. When you join Caterpillar, you're 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 don't 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. Cat Digital is the digital and technology arm of Caterpillar Inc., leveraging the latest technologies to build industry leading digital solutions for our customers and dealers. With over 1.5 million connected assets worldwide, our teams use data, technology, advanced analytics, telematics, and AI capabilities to help our customers build a better, more sustainable world.

As the Lead Business Intelligence Analyst , you will serve as a technical AI product analytics lead for the Cat AI Assistant. You will define how we measure product value, user behavior, experimentation, adoption, retention, quality of experience, and business impact across internal users and dealers. You will turn complex product and behavioral data into clear insights that help leaders make better decisions about roadmap priorities, enablement, governance, and product investment. This role operates as an individual contributor lead who establishes scalable analytics foundations, serves as a technical resource for analytical approaches, mentors others, anticipates the next questions leaders will ask, and helps the team move from dashboard creation to trusted product insight generation.

AI Product Analytics & Behavioral Measurement Serve as a lead technical expert for product analytics, offering guidance and support to move analytical work forward and ensure high-quality, trusted deliverables. Analyze usage patterns, behavioral trends, funnel performance, and areas of friction to identify what is driving or limiting product adoption. Gather, analyze, and interpret data from product telemetry, digital analytics platforms, surveys, interviews, support channels, dealer feedback, and other relevant sources to provide comprehensive product insights. Apply statistical thinking, segmentation, anomaly detection, cohort analysis, and other analytical methods to answer complex product and adoption questions. Dashboards, Reporting & Data Modeling Design and build product, adoption, and executive dashboards using Power BI, Tableau, or similar BI tools . Partner with data, engineering, and product teams to model reliable datasets in Snowflake or comparable data platforms . Use SQL and other analytical tools to validate data, investigate trends, and create repeatable analysis that can scale beyond one-time reporting. Create dashboard concepts, wireframes, or mockups to align stakeholders on metric intent, layout, and decision-making needs before building production reporting. Document data sources, joins, calculations, assumptions, and metric definitions so reporting is trusted, repeatable, and easy to maintain. Data Governance & Reporting Integrity Establish governance for adoption reporting, including metric definitions, refresh cadence, ownership, and data quality checks. Ensure accurate, timely, and auditable visibility of adoption metrics for internal teams and dealer audiences. Create repeatable documentation and standards to support scalable reporting. Experimentation, Product Learning & Decision Support Support experimentation and product learning plans, including A/B testing, pilot measurement, hypothesis definition, and success criteria. Help product teams connect analytics findings to product design, roadmap planning, feature development, launch decisions, and post-launch optimization. Evaluate the impact of enablement, launch, and engagement strategies using data, then provide clear