Director Data Annotation Ai Content Writer Jobs in Colorado

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 capabilityneeds.

• 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 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 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.

• Experience establishing or scaling product management, engineering, governance, and delivery practices.

• Experience leading product managers, product owners, or cross-functional teams responsible for distinct product domains.

• Experience operating within a federated or hub-and-spoke model and partnering with centralized enterprise technology teams.

• Experience delivering solutions within modern cloud data,lakehouse, integration, analytics, automation, or AI ecosystems.

• Experience managing external engineering, analytics, product, or technology delivery partners.

• Experience with operational, telemetry, asset, infrastructure, industrial, or data-center environments strongly preferred.

• Experience advancing capabilities from traditional reporting toward predictive analytics, intelligent automation, or AI-enabled solutions preferred.

Skills

• Strong understanding of product management, data product management, data engineering,Strong knowledge of product management, data engineering, analytics engineering, data architecture, semantic modeling, business intelligence, governance, automation, and AI enablement.

• Ability to build a product management capability that balances focused product ownership with coordinated portfolio governance.

• Ability to translate complex operational needs into clear product strategies, technical roadmaps, investment priorities, and executable delivery plans.

• Strong portfolio management and prioritization skills, including evaluating competing needs, dependencies, capacity, risk, readiness, and business value.

• Strong business and technical judgment, with the ability to balance immediate delivery needs with long-term capability development.

• Ability to lead through influence across business, technology, operational, enterprise platform, and external partner organizations.

• Strong executive communication, stakeholder management, negotiation, and decision-making skills.

• Ability to communicate complex data, product, technology, and AI concepts clearly to technical and nontechnical audiences.

• Strong people leadership skills, including organization design, talent development, coaching, performance management, and team building.

• High degree of ownership, accountability, curiosity, and comfort operating in a complex and evolving environment.