Lead Business Intelligence Analyst

Caterpillar Inc.•Peoria, IL

2d•$128,470 - $192,710

About The Position

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.

Requirements

Extensive experience analyzing product usage, user behavior, engagement, retention, digital adoption, or customer experience data.
Ability to define KPIs, success measures, and analytical frameworks that connect product behavior to business outcomes.
Strong analytical curiosity, including the ability to reassess the business problem, ask the next question, and follow the data to a better answer.
Hands-on experience with Power BI, Tableau, or similar visualization tools.
Experience working with Snowflake, enterprise data warehouses, or similar cloud data platforms.
Strong proficiency with SQL and comfort working with large, complex, or imperfect datasets; experience with Python, R, or similar analytical tools is preferred for deeper statistical analysis, automation, or repeatable analytical workflows.
Working knowledge of statistical methods, data mining, cohort analysis, segmentation, experimentation, A/B testing, anomaly detection, and other analytical approaches used to evaluate product behavior and business outcomes.
Experience with Python, R, or similar analytical tools is preferred.
Ability to translate complex data into clear, actionable insights for technical and non-technical stakeholders.
Experience producing data dictionaries, metric definitions, dashboard documentation, analytical methodology, data quality standards, and reporting guidance that improve trust, repeatability, and long-term maintainability.
Ability to write clear technical documentation, including assumptions, methodology, data limitations, and guidance for how metrics should be used.
Comfortable operating as a first-of-role individual contributor.
Strong cross-functional collaboration skills with product, analytics, and customer-facing teams.
Familiarity with AI-enabled digital products is a plus.
Typically, 7+ years or more of professional experience in analytics, business intelligence, product analytics, data science, digital product management, or a related field.
Experience with AI-enabled products, digital products, software engineering teams, or technology platforms.
Strong proficiency in data analysis tools and software such as Excel, SQL, Snowflake, Power BI, Tableau, Python, or R.
Experience with experimentation, A/B testing, pilot measurement, product discovery, or customer research methods.
Proven ability to lead multiple workstreams, develop scalable analytical approaches, and influence product decisions without needing direct authority.
Bachelor’s degree in Data Science, Statistics, Computer Science, Business Analytics, Product Management, or a related field, or equivalent working experience.

Nice To Haves

Experience with Python, R, or similar analytical tools is preferred.
Familiarity with AI-enabled digital products is a plus.

Responsibilities

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.
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.
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.
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 direction on the next steps supported by the evidence.
Present findings to leaders in a clear, practical way that connects analytical evidence to product decisions.
Partner closely with Product, DACS, Dealers, Customer Support, and Industry Partners to align on adoption insights and priorities.
Research industry trends and best practices in digital and AI adoption measurement and incorporate learnings into internal approaches.
Communicate findings clearly to technical and non-technical audiences.
Employee is also responsible for performing other job duties as assigned by Caterpillar management from time to time.