Director, Data Analytics Engineering

About this role

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, the division responsible for bringing technology and connected solutions to Caterpillar's world-famous yellow iron, is seeking a highly experienced and visionary Director, Data Analytics Engineering to lead the development and maturation of our enterprise Data Foundation & Readiness capabilities.  This leader will be responsible for establishing the technical, operational, and governance frameworks that enable trusted, scalable, and reusable data assets for Analytics, Artificial Intelligence (AI), and Machine Learning (ML) solutions across the enterprise.

As CAT Digital accelerates its AI and analytics strategy, ensuring high-quality, production-ready data has become a critical differentiator. This leader will drive the transformation of how data is sourced, engineered, validated, monitored, and consumed for AI and analytics purposes to support rapid innovation while maintaining enterprise-grade reliability and governance.

The Director will lead a team of analytics engineering managers and partner closely with Data Engineering, AI/ML Engineering, Product Management, Architecture, Platform Engineering, and business stakeholders to establish consistent practices, reusable frameworks, and scalable operating models that accelerate delivery across the AI & Analytics portfolio. This role requires a unique combination of technical depth, engineering leadership, organizational influence, and strategic vision.

What You Will Do

Establish Enterprise Data Foundation & Readiness Capability • Define and lead the enterprise strategy for the data foundation and readiness across analytics and AI solutions. • Build a sustainable capability that ensures data products and foundational datasets are accurate, discoverable, scalable, and AI-ready. • Establish enterprise standards for data quality, lineage, observability, validation, certification, and stewardship. • Develop reusable frameworks, accelerators, and engineering patterns that improve consistency across teams and AI use cases. • Create a roadmap that advances the maturity of data readiness capabilities and supports future AI ambitions.

Lead Data Quality Excellence • Own the vision and implementation of modern data quality practices across analytics and AI ecosystems. • Establish measurable data quality standards, policies, service-level objectives, and remediation processes. • Create reusable quality frameworks, severity models, monitoring capabilities, and operational controls. • Implement proactive data quality detection and prevention mechanisms rather than reactive issue management. • Drive adoption of data testing, data contracts, observability tooling, and automated quality controls throughout the development lifecycle.

Accelerate AI & Analytics Delivery • Ensure AI and analytics teams have consistent, trusted, and reusable access to high-quality data and engineered features. • Drive standardization of feature sourcing, feature management, and data servicing patterns for AI/ML use cases. • Reduce duplicated effort across teams through reusable assets, platforms, and shared engineering components. • Enable faster experimentation and iteration while maintaining quality, reliability, and governance standards. • Help transform how AI-enabled solutions move from concept to production.

Build Scalable Engineering Practices • Establish best practices for analytics engineering, testing, benchmarking, evaluation, deployment, and operational monitoring. • Drive engineering rigor across data pipelines, feature pipelines, semantic layers, and analytical products. • Implement scalable frameworks for validation and benchmarking of AI and analytics capabilities. • Improve production monitoring, operational visibility, and performance measurement of data products and AI solutions. • Partner with engineering leaders to define architecture standards and modernization strategies.

Drive Organizational Transformation • Clarify ownership and accountability across data, analytics, AI, and product teams. • Help evolve the operating model required to support modern AI and analytics delivery. • Promote AI-enabled ways of working and identify opportunities to increase team velocity through automation and intelligent tooling. • Foster a culture of engineering excellence, experimentation, continuous improvement, and learning. • Influence senior leaders across CAT Digital to adopt data-driven and AI-enabled practices.

Leadership & Talent Development • Lead, coach, and develop a high-performing team of Analytics Engineering leaders and their organizations. • Build organizational capabilities in analytics engineering, data quality engineering, and AI readiness. • Establish talent strategies, career frameworks, and technical growth opportunities. • Attract and retain top technical talent. • Serve as a trusted advisor and thought leader across CAT Digital.

What Success Looks Like Within the first 12-24 months, this leader will: • Establish a clearly defined data readiness capabilities for AI and analytics across CAT Digital. • Deliver enterprise-wide smart data quality tools and frameworks to improve quality for AI • Improve consistency and reuse of data assets supporting AI and analytics initiatives. • Reduce data-related production issues and accelerate issue resolution. • Increase trust, adoption, and reuse of enterprise data assets. • Improve AI and analytics solution delivery speed through stronger foundational

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