AI Data Engineer Jobs in Arizona

POSITION SUMMARY

Engineering & Intelligence is building a capability-based organization to support McCarthy’s next stage of growth. The Engineering, Data & AI pillar provides the technical capabilities required to build, integrate, operate, govern, and scale applications, Foundry, data, AI, and engineering platforms.

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.

NOTICE TO EXTERNAL SEARCH FIRMS: McCarthy’s Talent Acquisition Team is the only authorized representative permitted to engage with external search firms, staffing agencies, or other third-party recruiting partners. McCarthy maintains an Approved Agency List for recruiting partners, which is reviewed and updated annually.

McCarthy will only consider submissions from agencies with a signed fee agreement in place for the current year. McCarthy does not accept unsolicited resumes, candidate submissions, or referrals from agencies that do not meet these requirements.

If a candidate is submitted without an active agreement, McCarthy will have no obligation to pay any fees and reserves the right to contact, engage, interview, or hire such candidate(s) without any financial or other responsibility to the submitting agency. Unsolicited resumes, including those sent directly to hiring managers or other employees, will be considered the property of McCarthy.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.