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Vantage Data Centers

Wholesale data center campuses for hyperscalers

Director – Data Analytics & Engineering - Operations

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeDenver, CO, USA

About the Job

Requirements

  • A bachelor's degree in Computer Science, Data Engineering, Information Systems, Engineering, Business, Product Management, or a related field is required.
  • A master's degree in a related technical or business discipline is preferred.
  • Ten or more years of progressive experience in data analytics, data engineering, product management, analytics, enterprise data platforms, business intelligence, or a related discipline.
  • Five or more years of experience leading multidisciplinary functional or technical teams, programs, or data solution portfolios.
  • Demonstrated experience developing data strategies and roadmaps and delivering data, analytics, digital, or AI solutions tied to measurable business outcomes.
  • Experience establishing or scaling data management, engineering, governance, and delivery practices.
  • Experience leading or cross-functional teams responsible for distinct data 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, application, or technology delivery partners.
  • Strong knowledge of data engineering, analytics engineering, data architecture, semantic modeling, business intelligence, governance, automation, and AI enablement.
  • Experience delivering solutions across modern cloud data, lakehouse, integration, analytics, governance, automation, and AI ecosystems, with working knowledge of platforms and capabilities such as Microsoft Fabric/Azure, Databricks or Snowflake, Power BI, ServiceNow, Power Platform, Copilot Studio, data catalog, lineage, metadata management, data quality, access control, master data management, telemetry platforms, and operational systems such as CMMS/EAM.
  • Ability to build a capability that balances focused data solution ownership with coordinated portfolio governance.
  • Ability to translate complex operational needs into clear 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.
  • Ability to communicate complex data, solution, 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.
  • The employee must occasionally lift and/or move up to 25 pounds.

Responsibilities

  • 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.
  • 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.
  • Serve as the primary Operations partner to Global Data & AI, representing Operations priorities, dependencies, capacity requirements, and future capability needs.
  • 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.
  • 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.
  • 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 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.
  • 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.
  • Perform additional duties as assigned by management.

Desired Qualifications

  • Experience with operational, telemetry, asset, infrastructure, industrial, or data-center environments is strongly preferred.
  • Experience advancing capabilities from traditional reporting toward predictive analytics, intelligent automation, or AI-enabled solutions is preferred.
  • Relevant technical, data management, cloud-platform, product-management, or agile certifications are a plus.

About the Company

Vantage Data Centers

Wholesale data center campuses for hyperscalers

Vantage Data Centers builds and operates large-scale wholesale data center campuses for hyperscalers, cloud providers, and large enterprises. Its core product is flexible, scalable data center space (white space) within campuses that range from 64 to 150 megawatts of power, designed to be rapidly provisioned for clients’ growth (often within six months). The company uses standardized, flexible designs to ensure predictable performance, high efficiency, and minimized downtime, while prioritizing sustainability in partnership with clients. Vantage differentiates itself through its focus on large-scale wholesale campuses, rapid deployment, scalable capacity, and energy-efficiency commitments across its North American and European markets. Its goal is to help major customers quickly and reliably scale their IT infrastructure with energy-conscious, scalable data center space, backed by a strong investor base and a footprint in key markets.

Company size
1,001-5,000
Company stage
Debt Financing
Total funding
$29.1B
Headquarters
Denver, CO
Founded
2010

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