SVP, Chief Technology Officer (CTO

SVP, Chief Technology Officer (CTO)

The SVP, Chief Technology Officer (CTO) is the executive responsible for defining and executing the enterprise technology strategy through four strategic pillars:

• Cloud & Platform Engineering
• Enterprise Data Engineering
• Artificial Intelligence & Intelligent Automation
• Enterprise Architecture & Technology Innovation

The CTO is accountable for building and operating the secure, scalable, cloud-native technology platforms that enable the business to innovate, modernize, and compete. This includes enterprise cloud infrastructure, developer platforms, data platforms, AI platforms, integration capabilities, and shared technology services that support all business functions.

A critical responsibility of this role is ensuring Artificial Intelligence delivers measurable business value. The CTO partners with executive leadership to identify opportunities where AI can fundamentally redesign business processes, improve customer and employee experiences, accelerate decision-making, and create competitive advantage. Success is measured not by AI experimentation, but by enterprise adoption, business transformation, and quantifiable business outcomes.

The CTO works in partnership with the CIO to shape the long-term technology vision while ensuring technology investments improve enterprise agility, resiliency, operational efficiency, and innovation.

• Develop and execute the enterprise technology strategy aligned with business objectives and long-term growth.
• Establish technology roadmaps that modernize enterprise capabilities through cloud-native platforms, automation, data, and AI.
• Advise the organization on emerging technologies, technology investments, and innovation opportunities.
• Drive enterprise technology standards, governance, architecture, and technology lifecycle management.

Lead the strategy, engineering, and operation of enterprise technology platforms, including:

• Public and hybrid cloud infrastructure
• Platform Engineering
• Kubernetes and container platforms
• Infrastructure as Code (IaC)
• Dev Sec Ops  and CI/CD platforms
• Site Reliability Engineering (SRE)
• Enterprise observability and monitoring
• Identity and shared platform services
• Fin Ops and cloud optimization

Build highly automated, resilient, secure, and scalable technology platforms that accelerate software delivery while improving operational excellence and reducing total cost of ownership.

Define and lead the enterprise Data Engineering strategy supporting analytics, operational reporting, AI, and digital products.

Responsibilities include:

• Modern cloud-based data platforms
• Enterprise data lakehouse architecture
• Data governance and metadata management
• Data quality and lineage
• Master Data Management
• Enterprise data security

Demonstrated expertise is required across multiple enterprise data integration patterns including:

• Batch processing
• API-based integration
• Event-driven architectures
• Real-time streaming platforms

Ensure enterprise data is trusted, governed, secure, reusable, and accessible to support business intelligence and AI initiatives.

Develop and execute the enterprise AI strategy focused on measurable business value creation.

Responsibilities include:

• Identify high-value AI opportunities in partnership with business leaders.
• Consult with business executives to redesign business processes that leverage AI rather than simply automating existing human workflows.
• Lead enterprise implementation of Generative AI, Machine Learning, Intelligent Automation, and Agentic AI capabilities.
• Build and operate enterprise AI platforms supporting secure and scalable AI adoption.
• Establish Responsible AI governance, model lifecycle management, and AI risk management.
• Create repeatable frameworks for identifying, prioritizing, implementing, and scaling AI use cases across the enterprise.
• Drive enterprise AI literacy through executive education, workforce training, change management, communications, and adoption programs.
• Measure AI success through quantifiable business outcomes including productivity improvements, expense reduction, revenue growth, customer satisfaction, and operational efficiency.

Candidates must demonstrate a proven track record of leading enterprise AI implementations that progressed beyond pilot programs to production deployment with measurable business value.

Lead Enterprise Architecture to ensure technology investments create reusable enterprise capabilities rather than siloed solutions.

Drive innovation through:

• Emerging technology evaluation
• Technology incubation
• Strategic partnerships
• Cloud hyperscaler relationships
• AI ecosystem partnerships
• Technology standards and reference architectures

Guide enterprise modernization initiatives while ensuring technology investments remain aligned with business strategy.

Build and lead high-performing organizations…