Essential Job Functions 

 Data Product Strategy and Portfolio Management 

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

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

• Establish a product management operating model with clear roles, decision rights, lifecycle practices, and governance across product, engineering, business, and enterprise technology teams. 

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

Data Product and Engineering Delivery 

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

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

• Establish cross-functional product 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 product 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, product 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 capability needs. 

• Align the Operations data product 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 products, 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 product portfolio in alignment with enterprise policies and standards. 

• Partner with Operations Intelligence, product leads, business data product owners, 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, product health, adoption, value, and issue resolution. 

• Embed security, controls, quality assurance, and applicable risk and regulatory requirements throughout the product 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 products 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 with operations intelligence, 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 data product 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 Products & Engineering, Operations Intelligence, Global Data & AI, Operations functions, and other enterprise partners. 

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

• Ensure product leaders have sufficient authority to drive product 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. 

Experience 

• Ten or more years of progressive experience in data products, product management, data engineering, analytics, enterprise data platforms, business intelligence, or a related discipline. 

• Five or more years of experience leading multidisciplinary product or technical teams, programs, or product portfolios. 

• Demonstrated experience developing product strategies and roadmaps and delivering data, analytics, digital, or AI products tied to measurable business outcomes.