Head of Technology Data Management

TD•Mount Laurel, NJ

1d•$200,000 - $280,000•Onsite

About The Position

The Head of Technology Data Management is responsible for establishing and leading the enterprise capability for managing, governing, and operationalizing technology reference data across infrastructure, cyber, cloud, AI, and enterprise technology domains. The role will build and operate a Databricks-based data fabric that transforms fragmented technology data from multiple systems of record into a trusted, authoritative, and consumable enterprise asset. This leader will define and implement data governance, taxonomies, common data models, metadata standards, and data products that enable enterprise-wide reporting, analytics, risk management, FinOps, operational management, and executive decision-making. The role serves as the central authority for technology reference data, ensuring data is accurate, governed, discoverable, and accessible through modern consumption channels including APIs, Power BI, and enterprise reporting platforms.

Requirements

Bachelor's Degree in Computer Science, Engineering, Information Technology, Data Management, Information Systems, or a related discipline.
12+ years of progressive experience in enterprise data management, data architecture, analytics, data governance, or technology strategy.
7+ years in a senior leadership role managing large-scale data, technology, or transformation programs.
Proven experience building enterprise data lakes, data fabrics, or data platforms using Databricks or equivalent technology.
Deep expertise in data governance, metadata management, taxonomy development, and common data model design.
Experience delivering enterprise reporting and analytics solutions using Power BI or similar platforms.
Experience integrating complex technology data from multiple systems of record using APIs, event-driven architectures, and modern data integration patterns.
Strong understanding of infrastructure, cybersecurity, cloud, AI, and enterprise technology environments.
Experience operating in highly regulated industries and supporting audit, regulatory, and risk management requirements.
Demonstrated success influencing senior executives and leading cross-functional transformation initiatives.

Nice To Haves

Master's Degree preferred (MBA, Data Science, Information Management, or Enterprise Architecture).
Industry certifications such as Databricks, TOGAF, DAMA CDMP, ITIL, Cloud Provider Certifications (Azure, AWS, GCP) are preferred.

Responsibilities

Define and lead the enterprise strategy, operating model, and roadmap for technology reference data.
Establish a scalable technology data fabric leveraging Databricks as the enterprise platform for technology data integration and consumption.
Create a long-term vision for trusted technology data that supports enterprise governance, reporting, analytics, operational management, and risk oversight.
Drive enterprise adoption of common data standards, governance practices, and technology data products.
Establish governance frameworks to ensure technology data is accurate, complete, authoritative, compliant, and fit for purpose.
Define data ownership, stewardship, accountability, and quality controls across technology domains.
Implement controls, monitoring, and remediation processes to improve ongoing data quality.
Ensure data management practices align with regulatory, audit, risk, security, and compliance requirements.
Own the enterprise technology data dictionary and business glossary.
Establish and maintain metadata standards, data lineage, and data cataloguing capabilities.
Create a technology data marketplace that enables stakeholders to discover, understand, and consume enterprise data assets.
Drive consistent interpretation of technology data across the organization.
Define and maintain a common technology taxonomy spanning infrastructure, cyber, cloud, AI, applications, services, products, assets, risks, and capabilities.
Establish canonical data models and enterprise data relationships across technology domains.
Ensure technology data is standardized across systems and reporting platforms.
Drive consistency in definitions, classifications, hierarchies, and business rules.
Define and govern enterprise technology data products aligned to business and operational use cases.
Translate complex technical data into business-consumable products that support decision-making.
Establish standards for reusable data assets, semantic layers, APIs, and reporting services.
Prioritize data products based on business value, risk reduction, and strategic objectives.
Lead onboarding of technology systems of record into Databricks through APIs, event-based integrations, streaming capabilities, and zero-copy architectures.
Establish enterprise standards for data integration, ingestion, transformation, and observability.
Ensure scalable, resilient, and secure data pipelines that support enterprise consumption.
Partner with engineering teams to optimize platform performance and data delivery capabilities.
Establish enterprise reporting and analytics capabilities for technology management and governance.
Define KPI frameworks, measurement standards, and reporting methodologies.
Deliver trusted dashboards, scorecards, and executive reporting through Power BI and enterprise reporting platforms.
Support stakeholders in measuring operational performance, technology health, risk exposure, and strategic outcomes.
Ensure dashboards and data products are intuitive, actionable, and aligned to stakeholder needs.
Drive persona-based reporting strategies for executives, risk teams, infrastructure teams, cyber teams, engineers, and technology leaders.
Establish enterprise visualization and reporting standards.
Measure dashboard adoption, effectiveness, and business value realization.
Build strong partnerships across Infrastructure, Cybersecurity, Enterprise Architecture, Finance, Risk, Data, Audit, and Engineering teams.
Influence senior executives and stakeholders on enterprise data priorities and standards.
Lead cross-functional initiatives that require coordination across business and technology domains.
Champion a culture of data-driven decision-making and accountability.