Manager Data Analytics & AI

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.

Manages leaders and employees responsible for data, analytics, and AI enablement across development initiatives, data platforms, and application support functions. Provides portfolio‑level leadership to ensure data and analytics capabilities are scalable, reusable, and aligned to business outcomes.

What You Will Do:

• Oversee enterprise‑wide data, analytics, and AI enablement initiatives, including data product design, integration patterns, and AI‑ready data foundations.

• Provide strategic direction to ensure data, analytics, and AI capabilities meet business needs, decision requirements, and future scalability.

• Establish and enforce standards for data quality, lineage, governance, and reuse across the portfolio.

• Lead adoption of data fabric patterns to enable federated data access with enterprise‑wide guardrails.

• Partner with solution, integration, and data architects to ensure consistent implementation of data and AI patterns.

• Maximize staff and leader contribution through coaching, professional growth, and capability development.

• Monitor and maintain adherence to quality, security, and responsible AI standards on an ongoing basis.

• Ensure analytics and AI deliver actionable insights, not just reports or models.

What You Have:

Degree Requirement

BS/MS in Computer Science or equivalent experience desired.

Business Acumen — Level: Working Knowledge

• Understands the organization’s business model, value drivers, and financial goals.

• Communicates key considerations for business and technology decision‑making.

• Translates business objectives into analytics, data, and AI priorities.

• Engages stakeholders to balance competing priorities and outcomes.

Information Technology Trends — Level: Expert

• Participates in architecting enterprise‑wide data, analytics, and AI strategies.

• Champions awareness of emerging trends such as AI platforms, data fabric, data products, and advanced analytics.

• Ensures senior leadership awareness of the enterprise impact of major technology trends.

• Monitors industry best practices and guides responsible adoption of new technologies.

• Provides perspective on the evolution, risks, and business impact of AI‑driven capabilities.

Analytical Thinking — Level: Expert

• Designs and orchestrates the use of analytics and AI for strategic decision‑making.

• Champions advanced analytics, predictive insights, and prescriptive decision support.

• Enables leaders to use analytics insights effectively, not just consume dashboards.

• Implements outcome‑focused operating metrics and performance measures.

• Applies quantitative, statistical, and modeling approaches to complex business problems.

Data Fabric & AI‑Ready Data Foundations — Level: Expert

• Defines and operationalizes a data fabric strategy across distributed data platforms.

• Enables federated data ownership with centralized standards for security, quality, and semantics.

• Leverages metadata, lineage, and policy‑driven governance embedded directly in data flows.

• Ensures data assets are reusable, discoverable, and trusted for analytics and AI use cases.

• Aligns data fabric patterns with AI lifecycle needs, including feature reuse, explainability, and auditability.

Continuous Transformation — Level: Working Knowledge

• Leads adoption of new platforms, tools, and processes as business and technology evolve.

• Adapts operating models to support analytics‑driven and AI‑enabled ways of working.

• Encourages experimentation, learning, and continuous improvement.

• Communicates lessons learned from both successes and failures.

Talent Management — Level: Extensive Experience

• Builds and sustains a strong pipeline of data, analytics, and AI leadership talent.

• Coaches leaders