Sr. Director, Enterprise Data, Analytics, AI & Automation

Mazda Motor Corporation

1d•$228,600 - $348,600•Remote

About The Position

The Senior Director of Enterprise Data, Analytics, AI and automation is responsible for defining and executing the enterprise strategy for data, analytics, artificial intelligence, and intelligent automation. This leader will build the foundational capabilities, governance, platforms and operating models required to transform the data into a strategic asset, enable AI-powered business outcomes across the organization. Reporting to the Chief Information Officer, the role will partner closely with business leaders, technology teams, and external partners to modernize decision-making, automate business processes, accelerate innovation, and deliver measurable business value through data-driven intelligence. The successful candidate will lead the organization's Data & AI transformation agenda, ensuring that analytics, automation, machine learning, generative AI, and agentic AI capabilities are implemented responsibly, securely, and at scale.

Requirements

10+ years progressive leadership experience in enterprise Data, Analytics, AI, Automation, or Digital Transformation, with accountability for enterprise strategy, delivery, and business outcomes required
7+ years experience leading enterprise data platforms, cloud data architectures, analytics engineering, or data management organizations in a complex, matrixed enterprise required
5+ years experience leading enterprise AI, machine learning, advanced analytics, or intelligent automation initiatives, including operating model design, governance, and enterprise adoption required
5+ years experience with people leadership, organizational development, financial management, and strategic vendor management, including oversight of consulting partners, managed services, and technology providers required
Deep expertise in enterprise data governance, data quality management, metadata management, and modern data architecture (Advanced-Authority)
Demonstrated ability to develop, operationalize, and scale enterprise AI enablement programs, including use case identification, governance, change management, and cross-functional adoption (Advanced-Authority)
Strong understanding of advanced analytics, statistical modeling, machine learning, and AI technologies, with the ability to lead highly technical teams and guide architectural and technology decisions (Advanced-Authority)
Working knowledge of programming and query languages commonly used in modern data environments (e.g., Python, SQL, R; Java and C++) (Advanced-Authority)
Comprehensive knowledge of data privacy, cybersecurity, regulatory compliance, and emerging AI governance and Responsible AI frameworks (Advanced-Authority)
Proven experience managing strategic technology vendors, negotiating complex contracts, and overseeing large-scale data and AI platform partnerships (Advanced-Authority)
Executive presence with exceptional written and verbal communication skills; able to translate complex technical concepts into clear business insights for executive leadership and board-level audiences (Advanced-Authority)
Demonstrated ability to lead and influence across a complex, matrixed organization while building trusted relationships with business and technology stakeholders (Advanced-Authority)
Strong portfolio, program, and project management capabilities, with the ability to prioritize competing initiatives and deliver measurable business outcomes in a fast-paced environment (Advanced-Authority)
Collaborative and inclusive leadership style with the ability to coach teams, influence peers, and build alignment across executive and cross-functional stakeholders (Advanced-Authority)
Proficiency with Microsoft 365, Power BI, Copilot, and other enterprise AI productivity and collaboration platforms (Advanced-Authority)
Consistently demonstrates MNAO's Leadership Competencies and Pride Point behaviors while fostering a culture of innovation, accountability, collaboration, and continuous improvement (Advanced-Authority)

Nice To Haves

Demonstrated success developing and executing multi-year technology roadmaps, investment strategies, and enterprise transformation programs preferred
Experience establishing enterprise data governance, AI governance, information management, and regulatory compliance programs preferred
Experience leading modern cloud data platforms and analytics ecosystems (e.g., Azure, Databricks, Microsoft Fabric, AWS, or Google Cloud) preferred
Experience leading enterprise ERP, CRM, and business intelligence ecosystems to support enterprise reporting, analytics, and operational decision-making preferred
Experience working within global or multinational organizations; experience in a Japanese-affiliated organization preferred
Automotive, manufacturing, retail, or other complex multi-channel enterprise experience preferred
Microsoft Certified: Azure Data Fundamentals - Microsoft Corporation Preferred Upon Hire
AWS Certified Data Analytics – Specialty - Scrum Alliance Preferred Upon Hire
Databricks Certified Data Engineer Associate - Databricks Preferred Upon Hire
Google Cloud Professional Cloud Architect - Google Cloud Preferred Upon Hire
Certified Data Management Professional (CDMP) - Data Management Association International (DAMA) Preferred Upon Hire
Agile Certified Practitioner (PMI-ACP) - Project Management Institute (PMI) Preferred Upon Hire
Project Management Professional (PMP) - Project Management Institute (PMI) Preferred Upon Hire

Responsibilities

Define and execute the enterprise Data, Analytics, AI, and Automation strategy aligned with business priorities and long-term corporate objectives.
Develop and maintain a multi-year roadmap for data modernization, AI adoption, advanced analytics, and intelligent automation.
Establish enterprise investment priorities, performance metrics, and value realization frameworks for Data & AI initiatives.
Advise executive leadership on emerging technologies, AI trends, opportunities, and risks to enable informed strategic decisions and responsible innovation.
Prioritize the enterprise Data & AI portfolio in partnership with the business based on strategic alignment, business value, and organizational readiness.
Lead the strategy, architecture, development, and operation of the enterprise data platform, including the Enterprise Data Warehouse, Azure Data Lake, Databricks Lakehouse, integration services, and enterprise reporting environments.
Establish enterprise data governance, data quality, metadata management, master data management, and information protection capabilities.
Oversee enterprise data architecture, pipelines, integration patterns, and platform operations, ensuring reliability, scalability, security, compliance, and performance.
Deliver trusted, accessible, and reusable enterprise data products that accelerate analytics delivery and improve business decision-making.
Manage enterprise analytics platforms and visualization technologies, including Power BI and cloud-based analytics services.
Drive enterprise data democratization through secure, governed self-service access to trusted data and business insights.
Develop enterprise analytics capabilities that improve decision-making, customer outcomes, operational performance, and business growth.
Expand enterprise analytics and AI capabilities—including predictive analytics, machine learning, real-time intelligence, and self-service reporting—to improve decision-making and business outcomes.
Lead enterprise AI strategy, initiatives delivery across machine learning, generative AI, agentic AI, copilots, and intelligent digital capabilities, enabling new ways of working and to create business value.
Partner with business leaders to identify, prioritize, and scale high-value AI use cases across customer, dealer, employee, and enterprise operations.
Establish Responsible AI governance, policies, model lifecycle management, and AI risk management practices.
Measure adoption rate, business value, and operational impact of analytics and AI solutions through defined KPIs and value realization metrics.
Execute the enterprise strategy for intelligent automation and AI-enabled business transformation across business and technology functions.
Drive adoption of intelligent automation and AI capabilities, including RPA and AI-powered solutions, to improve productivity, efficiency, and employee experience.
Drive adoption of workflow automation and robotic process automation (RPA).
Lead enterprise deployment, governance, and adoption of AI platforms including Microsoft Copilot, Claude, and future enterprise AI capabilities.
Co-own the enterprise AI Workforce Strategy in partnership with Human Resources (HR) , establishing the operating model, governance framework, workforce readiness assessments, role impact analysis, capability-building programs, employee communications and change management required to scale Data/AI adoption responsibility across the enterprise.
Partner with Legal, Security, Risk, and IT to ensure the secure, ethical and compliant deployment and lifecycle management of AI technologies; serve as the technology co-owner alongside HR for all enterprise AI adoption, people readiness and organizational change programs.
Continuously evaluate emerging AI technologies, market trends, and vendor capabilities, translating innovation into scalable business solutions that create competitive advantage.
Establish shared metrics and governance with HR to track AI adoption rates, workforce capability growth, employee experience impact, and value realization across all AI enablement programs.
Sets strategic direction across multiple departments and broad functions, aligning team priorities with enterprise objectives to drive cohesive execution.
Build, lead, and develop a high-performing organization of data architects, engineers, analysts, data scientists, AI engineers, automation specialists, product owners, and technical leaders.
Lead relationships with strategic data platform vendors, 3rd party data providers, and partners, including contracts, licensing, and technical delivery.
Establish operating models, delivery frameworks, engineering standards, product management disciplines, and talent strategies that enable enterprise scale.
Foster a culture of innovation, operational excellence, collaboration, continuous learning, data literacy, AI literacy, and measurable business outcomes.
Manage the Data & AI investment portfolio, including budgets, resource planning, sourcing strategies, strategic vendor partnerships, vendor performance, and financial stewardship.
Champion cross-functional partnerships that accelerate enterprise transformation and maximize business value.
Perform other duties as assigned
Comply with all policies and standards