Director, Data Analytics & Engineering, Operations NA

The Director, Operations DataAnalytics& Engineeringleads themanagement and technical delivery capability for North America Operations. This role owns the integrated Operations dataportfolio and roadmap, translating operational priorities and intelligence requirements into trusted, scalable, and reusablesolutionsthat improve how Operations plans, executes, predicts, and makes decisions.

Reporting to the Vice President of Operational Excellence, the Director builds and leads a multidisciplinary capability spanningdeliverymanagement, data engineering, analytics engineering, business intelligence, governance, and solution delivery. As the portfolio evolves, the Director mayestablishdedicatedleadership for priority domains based on their scale, complexity, and strategic importance.

Serving as the primary Operations counterpart to Global Data & AI, the Director aligns Operations priorities with enterprise data and AI roadmaps, platforms, architecture, standards, and services. The role is accountable for Operations domaindatadeliverables, technical priorities,outcomes, and value realization whileleveraging, rather than duplicating, enterprise capabilities.

Essential Job Functions

Data Strategy and Portfolio Management

• Own and manage the integrated Operations data portfolio and multiyear roadmap.

• Translate Operations strategy, business priorities, and intelligence requirements into coordinated delivery strategies, use cases, investment priorities, and development plans.

• Establish and oversee dedicated leadership for priority functional domains as the portfolio matures, with accountability for data strategy vision, roadmaps, requirements, adoption, and value realization.

• Establish and oversee dedicated product leadership for priority domains as the portfolio matures, with accountability for product vision, roadmaps, requirements, adoption, and value realization.

• Implement a disciplined intake, evaluation, prioritization, and sequencing process for Operations data, analytics, engineering, automation, and AI needs.

• Make portfolio investment and capacity decisions based on operational value, strategic alignment, feasibility, risk, readiness, reuse, and available resources.

• Balance immediate delivery priorities with foundational investments in scalability, data quality, interoperability, and future capabilities.

• Manage data solutions throughout their lifecycle, from discovery and development through adoption, enhancement, sustainment, consolidation, or retirement.

Data Engineering & Delivery

• Lead the design and delivery of Operations data solutions, including governed pipelines, domain data models, semantic layers, telemetry integrations, analytics, dashboards, intelligent workflows, and AI-ready datasets.

• Translate data strategy and business requirements into scalable technical solutions in partnership with Global Data & AI, Enterprise Architecture, platform owners, and source-system teams.

• Establish cross-functional teams aligned to prioritized operational outcomes, with coordinated roadmaps, backlogs, release plans, and success measures.

• Develop reusable technical patterns and shared components that reduce fragmented development, one-off reporting, and duplicative solutions.

• Establish development and lifecycle practices that support solution reliability, performance, security, scalability, interoperability, maintainability, and user experience.

• Identify and resolve delivery dependencies, capacity constraints, architectural decisions, and cross-product conflicts.

• Oversee external delivery partners and vendors, ensuring accountability for technical quality, solution outcomes, knowledge transfer, and sustainable internal ownership.

Global Data & AI Alignment

• Serve as the primary Operations partner to Global Data & AI, representing Operations priorities, dependencies, capacity requirements, and future capabilityneeds.

• Align the Operations data roadmap with enterprise architecture, data platforms, shared engineering services, governance standards, security requirements, and AI strategy.

• Maintain clear accountability within the hub-and-spoke operating model, with Global Data & AI owning shared enterprise platforms and services and Operations owning its domain solutions, priorities, adoption, and outcomes.

• Coordinate decisions involving shared data sources, integrations, engineering capacity, platform constraints, common AI services, and cross-functional dependencies.

• Ensure Operations effectively leverages enterprise capabilities while avoiding disconnected, duplicative, or unsustainable solutions.

Data Governance and Product Quality

• Establish governance practices for the Operations data portfolio in alignment with enterprise policies and standards.

• Partner with Operations Intelligence, leads, business data owners, and stewards to define domain models, business rules, authoritative sources, ownership, and data-quality expectations.

• Ensure the technical implementation of approved KPI definitions, calculations, semantic models, decision logic, and reporting standards.

• Establish visibility into data quality, lineage, metadata, access, health, adoption, value, and issue resolution.

• Embed security, controls, quality assurance, and applicable risk and regulatory requirements throughout the development lifecycle.

Advanced Data and AI Capabilities

• Define the evolution of Operations capabilities from foundational reporting toward predictive and prescriptive insights, intelligent automation, and AI-enabled decision support.

• Establish reusable data, telemetry, integration, analytics, and AI foundations that support multiple capabilities, solutions and future use cases.

• Identify and advance opportunities involving forecasting, anomaly detection, intelligent workflows, automation, AI agents, and operational decision support.

• Evaluate prospective use cases withoperationsintelligence, product leads, and functional leaders based on operational value, feasibility, scalability, risk, and organizational readiness.

• Transition successful pilots and experiments into governed, secure, scalable, and supportable production capabilities.

• Monitor emerging data, AI, automation, telemetry, and operational technology practices relevant to data-center operations.

Organizational and Team Leadership

• Build and lead a multidisciplinary capability spanning lifecycle management, data engineering, analytics engineering, business intelligence, solution delivery, and data governance.

• Determine the appropriate mix of internal talent, enterprise services, embedded resources, and external delivery support.

• Establish clear roles, decision rights, and ways of working across Data Analytics & Engineering, Operations Intelligence, Global Data & AI, Operations functions, and other enterprise partners.

• Recruit, develop, and coach functional and technical professionals capable of owning complex solutions and influencing senior business and technology stakeholders.

• Ensure team members have sufficient authority to drive solution outcomes within appropriate portfolio, architecture, investment, and enterprise governance.

• Set clear performance expectations and foster a culture of accountability, curiosity, customer focus, technical excellence, and continuous improvement.

• Build strong working relationships with senior Operations, Technology, Product, Reliability Engineering, and enterprise data leaders.

Additional Duties

• Perform additional duties as assigned by management.

Job Requirements

Education

• Bachelor's degree in Computer Science, Data Engineering, Information Systems, Engineering, Business, Product Management, or a related field required.

• Master's degree in a related technical or business discipline preferred.

• Relevant technical, data management, cloud-platform, product-management, or agile certifications are a plus.