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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Director, Enterprise Data - **Company:** Flexential Corp. - **Location:** Denver, CO, United States - **Experience:** Experienced - **Salary:** $175,000.0 - $200,000.0 - **Contract:** Permanent contract - **Skills:** Active Directory, Application Programming Interfaces (APIs), Artificial Intelligence, Business Analytics Applications, Audit Trail, C Sharp (Programming Language), Software as a Service, Databases, Data Architecture, Data Centers, Information Engineering, Data Governance, Data Infrastructure, Data Integration, Data Integrity, Extract Transform Load (ETL), Data Security, Data Warehousing, Digital Assets, Dimensional Modeling, Electronic Data Interchange (EDI), Identity and Access Management, Information Lifecycle Management, Information Management, Interoperability, Python (Programming Language), Machine Learning, Metadata, Meta-Data Management, Microsoft Software, Microsoft SQL Server, Netsuite, Operational Data Store, Pentaho Data Integration, Scrum Methodology, Reference Data, Cloud Services, Microsoft Copilot, Anaplan, Salesforce.Com, SQL Stored Procedures, Data Streaming, Systems Integration, Enterprise Data Management, Data Logging, Data Classification, Cyberark, Retrieval-Augmented Generation, Boomi, Data Strategy, Data Analytics, Qlikview, Enterprise Integration, Operational Systems, Data Management, Api Management, Workday, Servicenow - **Published:** October 4, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=03024072ab084aa5 ## About the Role * 8+ years of progressive experience across enterprise data architecture, data engineering, integration, data governance, analytics, or related disciplines, including 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 to 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. * Strong written and verbal communication skills and the ability to influence senior business and technology stakeholders without relying solely on formal authority. * Demonstrated ability to manage competing priorities, operate within budget constraints, and maintain effectiveness in a fast-moving operational environment. Preferred Qualifications * Experience technologies such as 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 working within clearly separated 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. * A pragmatic leader-builder who can set strategy, make architecture decisions, coach a team, and personally work through ambiguity when the organization needs direction. * Balances enterprise standards with speed and recognizes that good governance creates clarity, trust, and reuse rather than process for its own sake. * Understands the full enterprise data lifecycle and can connect source-system design, integration, storage, transformation, quality, metadata, reporting, and AI readiness. * Comfortable challenging fragmented designs, duplicated logic, weak ownership, and unmeasured work while proposing practical alternatives. * Trusted by business and technology partners for sound judgment, transparent tradeoffs, follow-through, and an ownership mindset. * Thrives in an agile environment that uses Scrum, sprint planning, visible work management, and measurable outcomes to organize and accelerate delivery. * Builds a high-performance culture grounded in accountability, curiosity, documentation discipline, operational resilience, reuse, and continuous improvement., Applicants must be legally authorized to work in the United States and must reside in a state where the company maintains employment eligibility at the time of hire and throughout their employment. State eligibility may be subject to change based on business needs and regulatory requirements. ## Description * Develop, maintain, and execute the enterprise data strategy and multi-year roadmap aligned to Flexential priorities, measurable business outcomes, and AI readiness requirements. * Lead the evolution of Flexential's data capabilities from report-centric delivery toward governed data products, trusted enterprise metrics, reusable business capabilities, self-service analytics, automation, and emerging AI enablement that improve operational efficiency, accelerate decision-making, and deliver measurable business value. * Establish a clear operating model for the Data organization, 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 data platform, integration, governance, and data product initiatives, with clear outcomes, owners, dependencies, and measures of value. * Guide investment and rationalization decisions across the data ecosystem, favoring reuse, platform standards, and incremental modernization over unnecessary tool proliferation or wholesale replacement. * Partner with the AI vertical to identify the minimum viable governed data needed for priority AI use cases and sequence data work according to business value and readiness. * Collaborate with Enterprise Architects to ensure enterprise data architecture, integrations, models, and governance standards align with Flexential's broader technology strategy and future-state architecture. * Own the future-state enterprise data architecture and the standards that govern how data is sourced, moved, stored, transformed, secured, documented, and consumed. * Provide architectural leadership across the SQL Server-based Enterprise Data Warehouse (EDW) and Operational Data Store (ODS), including landing, staging, dimensional, fact, reporting, and operational schemas. * Establish and enforce data modeling, schema design, master and reference data, data lifecycle, interoperability, performance, reliability, and scalability standards. * Define authoritative systems of record and approved patterns for analytical, operational, and third-party data exchange. * Ensure architecture decisions support 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 clear separation of tool responsibilities: Boomi as the preferred platform for application-to-application, API, event-driven, and near-real-time operational integrations; Pentaho Data Integration as the primary platform for batch analytics ingestion and data warehouse population; and custom code only for justified exceptions. * Establish reusable integration patterns, data contracts, error handling, retry, logging, monitoring, testing, deployment, and support standards. * Oversee SQL Server stored procedures and other in-database transformations used for complex or performance-sensitive processing, ensuring they remain maintainable, documented, and appropriately scoped. * Partner with application owners for Salesforce, NetSuite, ServiceNow, Workday, Anaplan, and other source or target 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 clear Data Owner, Data Steward, Domain Lead, and technical custodian responsibilities. * Create lightweight 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 KPIs. * 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 data access, classification, retention, protection, and auditability with enterprise requirements. * Create transparent escalation and remediation processes for data quality issues and conflicting metric definitions, with accountability assigned to the appropriate business owner. * Drive the transition from ad hoc, report-centric delivery toward 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 ensure enterprise metrics are governed consistently across consumption platforms. * Enable self-service analytics through certified datasets, documented definitions, reusable data assets, clear guardrails, fit-for-purpose tooling, data literacy initiatives, and AI-assisted analytics capabilities that reduce dependency on centralized report development. * Ensure executive and cross-functional reporting is supported by 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 clear priorities, role expectations, delivery standards, and individual accountability. * Establish disciplined delivery and operational practices across Boomi, Pentaho, SQL Server, APIs, integrations, and data products, including 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 clearly to technical teams, business leaders, and executives. * Perform other duties as required and assigned. What Success Looks Like 12-Month Expectations * Develop and execute a prioritized enterprise data roadmap that clearly connects platform, architecture, integration, governance, reporting, and AI-readiness work to business outcomes. * Establish the initial enterprise data governance operating model, with named business Data Owners and Data Stewards for priority domains, clear decision rights, an active governance cadence, and a documented issue-escalation process. * Document the current-state and target-state enterprise data architecture, authoritative systems of record, critical data flows, approved integration patterns, and the modernization sequence for the EDW, ODS, and integration ecosystem. * Implement measurable data quality, pipeline reliability, platform health, and delivery performance reporting for critical data products and integrations. * Reduce reconciliation and rework for priority metrics by establishing governed definitions, traceable transformations, and reusable certified datasets or semantic layers. * Formalize data-team operating practices for intake, prioritization, design review, testing, deployment, documentation, production support, incident management, and root cause analysis. * Establish a repeatable data-readiness partnership with the AI vertical so priority AI use cases have documented source data, ownership, classification, quality, access, metadata, lineage, and ongoing monitoring requirements. 24-Month Trajectory * Operate a durable federated data governance model in which business domains own meaning and quality while the Enterprise Data team provides standards, platforms, architecture, and enablement. * 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. * Demonstrate sustained improvement in data reliability, timeliness, discoverability, trust, delivery speed, and measurable value delivered to business and AI initiatives. * Advance the enterprise data platform through deliberate modernization that improves scalability, observability, security, and supportability without disrupting critical operations. * Build a capable, accountable Enterprise Data team with clear roles, strong technical and business partnerships, documented institutional knowledge, and visible succession depth.