Director, Data Analytics Engineering

Caterpillar Inc.•Chicago, IL

1d•$189,080 - $283,630

About The Position

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.

Requirements

Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or related field.
12+ years of progressive experience in data engineering, analytics engineering, software engineering, analytics platforms, or related technical disciplines.
5+ years leading managers and large technical teams.
Experience building and scaling enterprise data platforms and analytics capabilities.
Deep expertise in data quality, data governance, data observability, and data lifecycle management.
Experience supporting AI/ML, advanced analytics, and digital product ecosystems.
Demonstrated success leading enterprise transformation initiatives.
Strong understanding of cloud data platforms, modern data architectures, and engineering best practices.
Proven ability to influence senior executive stakeholders.

Nice To Haves

Advanced degree in Computer Science, Engineering, Data Science, Analytics, or related field.
Expertise with modern cloud-based analytics ecosystems.
Experience implementing data quality frameworks, data contracts, cataloging, lineage, and observability platforms.
Experience supporting MLOps, AI platforms, feature stores, and production AI solutions.
Familiarity with product operating models, agile delivery, and platform engineering concepts.
Experience leading large-scale modernization efforts in complex enterprise environments.

Responsibilities

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.
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.
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.
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.
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.
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.