Director, Data Engineering & Data Science Engineering

RBGlobal•Westchester, IL

About The Position

IAA is seeking a Director of Data Engineering & Data Science Engineering to lead a highly visible, business-critical function at the intersection of data, analytics, machine learning, and business transformation. This leader will define and drive the vision, architecture, and execution for IAA’s data engineering and data science capabilities, ensuring the organization can scale advanced analytics, BI, forecasting, machine learning, and AI solutions that directly support business growth and operational excellence. This role requires a strong technical leader and business problem solver who can partner across a broad set of stakeholders including Operations, Business, Sales, Marketing, Product, and Engineering. The ideal candidate brings deep expertise in the Azure BI and data ecosystem, strong people leadership, and the ability to translate complex business needs into practical, scalable data and AI solutions. This position reports directly to the VP of Engineering and is a critical, high-visibility leadership role within the organization.

Requirements

Proven experience leading Data Engineering, BI, Analytics, and/or Data Science Engineering teams at the Director level or equivalent
Deep expertise in the Azure BI / data technology stack, including: Microsoft Fabric, Azure Synapse Analytics, Power BI, Broader Azure data and analytics services
Strong understanding of data engineering architecture, modern analytics platforms, and scalable data pipelines
Strong foundation in data science, machine learning, and model operationalization
Demonstrated ability to solve complex business problems through data, analytics, and technical leadership
Strong mentoring, coaching, and people leadership skills with experience growing high-performing technical teams
Excellent communication and stakeholder management skills; able to work effectively with a wide range of technical and non-technical partners such as Ops, Business, Sales, Marketing, Product, Engineering
Ability to operate successfully in a fast-paced, high-visibility environment with multiple priorities and stakeholders
Strong executive presence and the ability to connect technical decisions to business outcomes

Nice To Haves

Experience supporting enterprise use cases across operations, commercial functions, and product-driven organizations
Experience driving both BI modernization and data science / ML adoption within the same organization
Familiarity with cloud-native engineering practices, production-grade data platforms, and secure, scalable AI/ML environments
Experience leading organizations that combine data engineering, analytics engineering, BI, and data science under one leadership model

Responsibilities

Lead the Data Engineering and Data Science Engineering function for IAA, setting technical vision, delivery strategy, and operating rhythm
Build and evolve scalable data platforms, BI architecture, and ML-enablement capabilities using the Azure data and analytics stack
Drive strategy and execution across Microsoft Fabric, Synapse, Power BI, Azure BI technologies, and modern cloud data platforms
Partner with business and functional leaders to solve high-value problems across Operations, Sales, Marketing, Product, and other key areas
Guide the design and implementation of robust pipelines, semantic models, dashboards, self-service analytics, forecasting solutions, and machine learning systems
Help shape the roadmap for advanced analytics, predictive modeling, experimentation, and AI-driven insights
Mentor, coach, and grow data engineering and data science talent while raising the technical bar across the team
Establish strong engineering practices across architecture, delivery quality, scalability, governance, and operational excellence
Collaborate closely with engineering leaders and cross-functional teams to ensure data and AI solutions are aligned with platform, product, and business priorities
Act as a senior thought partner to leadership on data strategy, technical tradeoffs, and investment priorities