Director, Engineering, Data & AI

Information Technology—St. Louis,MODallas,TXHouston,TXPhoenix,Arizona

POSITION SUMMARY

The Director, Engineering, Data & AI will lead the combined Engineering, Data & AI capability within Engineering & Intelligence. This leader will establish the organization, operating model, talent strategy, technical accountability, and delivery discipline required to build and operate McCarthy’s technology capabilities at enterprise scale.

The scope includes Applications & Integration, Foundry Engineering, Engineering Enablement, Data Engineering, Data Management, AI Engineering, DataOps/MLOps, and the interfaces to platform administration, governance, FinOps, and reporting.

The Director will create a coherent engineering system across applications, Foundry, data, AI, and platform capabilities while maintaining clear boundaries with Enterprise Architecture, Digital Products & Platforms, Cybersecurity, and Technical Program Management & Delivery.

This role is accountable for how the organization builds, delivers, and operates technology. This role owns engineering and operational execution.

RESPONSIBILITIES

Pillar strategy and operating model

• Define and execute the Engineering, Data & AI capability strategy in support of McCarthy’s business and technology priorities.
• Establish clear ownership across applications, integrations, Foundry, data, AI, engineering enablement, and platform-related operations.
• Design an operating model that supports shared platforms, cross-team dependencies, production ownership, and a consistent engineering lifecycle.
• Translate enterprise priorities into a practical portfolio of capability investments, workforce needs, technical roadmaps, and delivery outcomes.
• Establish decision rights, escalation paths, operating rhythms, and performance measures across the pillar.
• Build the organization for scale, including leadership depth, internal talent development, partner strategy, and succession planning.

Engineering and platform leadership

• Oversee Applications & Integration, Foundry Engineering, Engineering Enablement, CI/CD, testing, reliability, production support, and engineering standards.
• Ensure application and Foundry engineering operate with shared standards, appropriate technical boundaries, and effective cross-team collaboration.
• Establish practices that improve software quality, delivery predictability, reliability, observability, security, and operational readiness.
• Partner with platform administration on governance, FinOps, environment readiness, access, platform health, and operating controls.
• Guide the transition from partner-supported delivery to durable McCarthy-owned engineering capabilities.
• Ensure engineering investments are aligned to business value, enterprise architecture, capacity, and production needs.

Data and AI leadership

• Oversee Data Engineering, Data Management, AI Engineering, DataOps, and MLOps through the Senior Manager, Data Engineering & AI and other leaders.
• Establish the data and AI engineering lifecycle from data acquisition and management through production deployment, monitoring, governance, and retirement.
• Improve the reliability, quality, discoverability, security, and reuse of enterprise data and AI capabilities.
• Ensure AI and data solutions are production-ready, observable, maintainable, and aligned with responsible-use and security expectations.
• Partner with Enterprise Architecture on data, AI, solution, integration, and platform architecture decisions.
• Connect Foundry, data, AI, and application engineering so that shared capabilities are designed for reuse rather than isolated point solutions.

Product and enterprise partnership

• Partner with Digital Products & Platforms to translate business outcomes and product priorities into feasible technical strategies and delivery plans.
• Partner with Enterprise Architecture to shape solutions early and resolve architectural tradeoffs with clear accountability.
• Partner with Technical Program Management & Delivery to improve demand shaping, prioritization, sequencing, dependency management, release readiness, and executive visibility.
• Partner with Cybersecurity, business stakeholders, vendors, and strategic technology partners to manage risk and enable delivery.
• Communicate technical strategy, tradeoffs, risks, investment needs, and delivery health to executive stakeholders.

Talent, culture, and leadership

• Hire, develop, and retain strong leaders and technical talent across engineering, data, AI, and platform capabilities.
• Establish a culture of accountability, collaboration, technical excellence, continuous learning, and “We, Not I” behavior.
• Create career paths for engineers, architects, data professionals, AI engineers, and technical leaders.
• Set expectations for inclusive leadership, thoughtful decision-making, and transparent communication.
• Balance near‑term delivery with long‑term capability development and technical sustainability.

QUALIFICATIONS

• Bachelor’s degree in computer science, engineering, information systems, data science, or equivalent practical experience. Master’s degree preferred.
• Minimum 12 years senior leadership experience across software engineering, data engineering, AI engineering, technology platforms, or a closely related enterprise technology domain.
• 5+ years of technical people leadership and experience leading through managers or senior technical leaders.
• Demonstrated success designing and operating technology organizations through growth, transformation, platform modernization, or operating‑model change.
• Strong understanding of software engineering, cloud platforms, data platforms, AI/ML engineering, integration, DevOps, reliability, security partnership, and production operations.
• Experience translating business strategy into technology strategy, organizational design, investment priorities, and measurable execution.
• Strong executive communication, stakeholder management, talent leadership, and change‑leadership skills.
• Ability to lead through influence across Product, Architecture, Cybersecurity, Finance, Operations, vendors, and business stakeholders.

Preferred qualifications

• Experience scaling an enterprise engineering, data, and AI organization in a complex or regulated environment.
• Experience with modern data platforms, AI/ML platforms, MLOps, cloud-native engineering, enterprise integration, or platform engineering.
• Experience with Palantir Foundry or comparable platforms is helpful but not required.
• Experience transitioning from contractor- or partner‑led delivery to an internal engineering operating model.

McCarthy is proud to be an equal opportunity employer, including disability and protected veteran status.