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Flexential

Hybrid IT services with data centers

Director – Enterprise Data

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

About the Job

Requirements

  • At least 8 years of progressive experience across enterprise data architecture, data engineering, integration, data governance, analytics, or related disciplines, including at least 3 years leading teams or significant cross-functional data programs.
  • Demonstrated experience defining and executing enterprise data strategies, roadmaps, operating models, architecture standards, and governance programs in a complex environment.
  • Strong knowledge of data warehousing, operational data stores, dimensional modeling, ETL/ELT, APIs, data integration, metadata, lineage, data quality, semantic models, and governed data products.
  • Experience leading enterprise integration and data engineering capabilities using platforms such as Boomi, Pentaho Data Integration, SQL Server, or comparable technologies.
  • Ability to distinguish and govern operational integration, analytical ingestion, in-database transformation, and custom-code patterns based on business and technical requirements.
  • Experience establishing business data ownership, stewardship, metric governance, issue resolution, and cross-functional decision forums.
  • Proven ability to lead managers, engineers, architects, analysts, or governance professionals and improve delivery discipline and operational maturity.
  • Ability to translate complex data architecture, quality, and governance issues into concise business risks, tradeoffs, decisions, and implementation plans.
  • Demonstrated ability to manage competing priorities, operate within budget constraints, and maintain effectiveness in a fast-moving operational environment.
  • Strong written and verbal communication skills and the ability to influence senior business and technology stakeholders without relying solely on formal authority.
  • Applicants must be legally authorized to work in the United States and reside in a state where the company maintains employment eligibility at hire and throughout employment.
  • Applicants must reside and perform work in states where the company is authorized to do business and maintain employment registrations; applicants residing or working in the listed excluded states and Washington, DC are not eligible.

Responsibilities

  • Define, maintain, and execute the enterprise data strategy and multi-year roadmap aligned to company priorities, measurable outcomes, and AI readiness.
  • Lead the evolution from report-centric delivery toward governed data products, trusted enterprise metrics, reusable business capabilities, self-service analytics, automation, and AI enablement.
  • Establish the Data organization operating model, including decision rights, service ownership, delivery intake, prioritization, capacity planning, and engagement with business and technology teams.
  • Translate enterprise priorities into a sequenced portfolio of platform, integration, governance, and data product initiatives with outcomes, owners, dependencies, and value measures.
  • Guide investment and rationalization decisions across the data ecosystem, favoring reuse, standards, and incremental modernization.
  • Partner with the AI vertical to identify governed data needs for priority AI use cases and sequence work according to business value and readiness.
  • Collaborate with Enterprise Architects to align enterprise data architecture, integrations, models, and governance standards with broader technology strategy and future-state architecture.
  • Own future-state enterprise data architecture and standards for sourcing, moving, storing, transforming, securing, documenting, and consuming data.
  • Provide architectural leadership across the SQL Server-based Enterprise Data Warehouse and Operational Data Store, including landing, staging, dimensional, fact, reporting, and operational schemas.
  • Establish and enforce standards for data modeling, schema design, master and reference data, lifecycle, interoperability, performance, reliability, and scalability.
  • Define authoritative systems of record and approved patterns for analytical, operational, and third-party data exchange.
  • Ensure architecture supports governed data products, reusable semantic layers, historical analysis, operational reporting, automation, and AI consumption.
  • Lead the design, delivery, operation, and continuous improvement of enterprise data pipelines, APIs, integrations, transformations, and orchestration workflows.
  • Maintain tool responsibilities: Boomi for application-to-application, API, event-driven, and near-real-time operational integrations; Pentaho Data Integration for batch analytics ingestion and warehouse population; custom code only for justified exceptions.
  • Establish reusable integration patterns and standards for data contracts, error handling, retries, logging, monitoring, testing, deployment, and support.
  • Oversee SQL Server stored procedures and other in-database transformations, ensuring they are maintainable, documented, and appropriately scoped.
  • Partner with application owners for Salesforce, NetSuite, ServiceNow, Workday, Anaplan, and other systems to improve data reliability and reduce brittle point-to-point dependencies.
  • Establish and mature a business-owned, enablement-first enterprise data governance program with Data Owner, Data Steward, Domain Lead, and technical custodian responsibilities.
  • Create policies, standards, and decision forums for data ownership, access, quality, definitions, classification, lifecycle management, issue resolution, and change control.
  • Develop an enterprise business glossary and consistent definitions for critical data elements, metrics, dimensions, and key performance indicators.
  • Advance metadata management, data cataloging, end-to-end lineage, data quality measurement, certification, and discoverability of governed data assets.
  • Partner with Security, Privacy, Risk, Compliance, Legal, and business leaders to align access, classification, retention, protection, and auditability with enterprise requirements.
  • Create escalation and remediation processes for data quality issues and conflicting metric definitions, with accountability assigned to the appropriate business owner.
  • Drive the transition to governed, reusable data products and semantic layers that support consistent reporting and analysis.
  • Partner with FP&A and business teams using Qlik, Anaplan, application-native reporting, and other analytical tools to govern enterprise metrics consistently across consumption platforms.
  • Enable self-service analytics through certified datasets, documented definitions, reusable data assets, guardrails, fit-for-purpose tooling, data literacy initiatives, and AI-assisted analytics capabilities.
  • Ensure executive and cross-functional reporting uses trusted source data, documented transformations, defined refresh expectations, and named business owners.
  • Improve data literacy and fluency by helping leaders and teams understand data definitions, quality, appropriate use, and accountability.
  • Lead, coach, and develop the Enterprise Data team, setting priorities, role expectations, delivery standards, and individual accountability.
  • Establish delivery and operational practices across Boomi, Pentaho, SQL Server, APIs, integrations, and data products for solution design, development, testing, release, observability, documentation, incident response, and root cause analysis.
  • Create measurable service health, platform reliability, delivery throughput, data quality, reuse, customer experience, and business value metrics.
  • Manage vendors, contracts, licensing, platform roadmaps, and external partners within the approved budget.
  • Communicate architecture decisions, delivery risks, tradeoffs, progress, and outcomes to technical teams, business leaders, and executives.
  • Perform other duties as required and assigned.
  • Within 12 months, develop and execute a prioritized enterprise data roadmap connecting platform, architecture, integration, governance, reporting, and AI-readiness work to business outcomes.
  • Within 12 months, establish the initial governance operating model, including named business Data Owners and Data Stewards for priority domains, decision rights, governance cadence, and issue escalation.
  • Within 12 months, document current- and target-state architecture, authoritative systems of record, critical data flows, approved integration patterns, and modernization sequence for the EDW, ODS, and integration ecosystem.
  • Within 12 months, implement measurable data quality, pipeline reliability, platform health, and delivery performance reporting for critical data products and integrations.
  • Within 12 months, reduce reconciliation and rework for priority metrics through governed definitions, traceable transformations, and reusable certified datasets or semantic layers.
  • Within 12 months, formalize data-team practices for intake, prioritization, design review, testing, deployment, documentation, production support, incident management, and root cause analysis.
  • Within 12 months, establish a repeatable data-readiness partnership with the AI vertical, covering source data, ownership, classification, quality, access, metadata, lineage, and monitoring for priority AI use cases.
  • Within 24 months, operate a federated data governance model in which business domains own meaning and quality while the Enterprise Data team provides standards, platforms, architecture, and enablement.
  • Within 24 months, increase reuse of governed data products, pipelines, integration components, and enterprise metrics while reducing redundant transformations, brittle point-to-point integrations, and direct-to-source reporting.
  • Within 24 months, demonstrate sustained improvements in data reliability, timeliness, discoverability, trust, delivery speed, and measurable value for business and AI initiatives.
  • Within 24 months, modernize the enterprise data platform to improve scalability, observability, security, and supportability without disrupting critical operations.
  • Within 24 months, build an accountable Enterprise Data team with clear roles, strong technical and business partnerships, documented institutional knowledge, and succession depth.

Desired Qualifications

  • Experience with Boomi, Pentaho Data Integration, Microsoft SQL Server, EDW/ODS architectures, stored procedures, Qlik, C#, and Python.
  • Experience integrating enterprise platforms such as Salesforce, NetSuite, ServiceNow, Workday, Anaplan, and other SaaS or operational systems.
  • Experience with data catalog, metadata, lineage, data quality, observability, master data, API management, and data classification capabilities.
  • Experience supporting data foundations for advanced analytics, intelligent automation, machine learning, retrieval-augmented generation, copilots, agents, decision intelligence, or enterprise AI capabilities while maintaining separate Data and AI accountabilities.
  • Familiarity with security and access management practices involving Active Directory, CyberArk, role-based access, credentials, audit logging, retention, and regulated or sensitive data.
  • Experience in colocation, data center, infrastructure, telecommunications, managed services, or another operationally complex industry.
  • Relevant certifications in data architecture, data management, data governance, cloud data platforms, Boomi, Microsoft technologies, or comparable disciplines are valued.
  • Ability to set strategy, make architecture decisions, coach a team, and personally work through ambiguity when direction is needed.
  • Ability to balance enterprise standards with delivery speed and use governance to create clarity, trust, and reuse.
  • Understanding of the enterprise data lifecycle and ability to connect source-system design, integration, storage, transformation, quality, metadata, reporting, and AI readiness.
  • Ability to challenge fragmented designs, duplicated logic, weak ownership, and unmeasured work while proposing practical alternatives.
  • Sound judgment, transparent tradeoffs, follow-through, and an ownership mindset with business and technology partners.
  • Experience working in an agile environment using Scrum, sprint planning, visible work management, and measurable outcomes.
  • Ability to build a high-performance culture grounded in accountability, curiosity, documentation discipline, operational resilience, reuse, and continuous improvement.

About the Company

Flexential

Hybrid IT services with data centers

Flexential provides tailored hybrid IT solutions, including colocation, cloud, connectivity, data protection, and managed services through its FlexAnywhere platform. It operates over 40 data centers in 19 U.S. markets, totaling more than three million square feet, serving enterprises in healthcare, financial services, and manufacturing. The FlexAnywhere platform offers a unified interface to manage infrastructure, connect to major cloud providers like AWS and Google Cloud, and scale resources on demand, enabling customers to run high-density computing and complex IT workloads. The company differentiates itself through its national footprint, focus on flexibility and customer needs, and an integrated suite of hybrid IT services delivered via a single platform. Its goal is to help clients digitally transform by providing customizable, scalable IT infrastructure that supports initiatives like AI and machine learning and adapts to changing business requirements.

Company size
501-1,000
Company stage
Debt Financing
Total funding
$833.4M
Headquarters
Centennial, CO
Founded
2000

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