Lead Data Governance Architect

Lead Data Governance Architect

Arlington, VA

Kapitus is seeking a high-impact, hands-on Lead Data Governance Architect to serve as the enterprise governance architect and primary practitioner for its Enterprise Data Governance program. Reporting to the VP, Data Governance & BI within the CDAO organization, this senior individual-contributor role is designed for a leader who balances enterprise framework design with practical execution. The role combines enterprise governance architecture, functional leadership, and hands-on operationalization.

The Lead Data Governance Architect will translate policy and data standards into adoptable operating practices across business and technology teams. This role will establish the governance foundation for trusted, well-understood, traceable, appropriately managed data. Will be responsible for stewardship, metadata, data quality, classification, retention, lineage, and governed analytical assets.

Working closely with Business Data Owners, Business Data Stewards, Technical Data Custodians, Data Engineering, Analytics Engineering, BI, Risk, Compliance, Legal, Information Security, and other partners, this role will help make governance a sustainable, shared organizational capability rather than a centralized bottleneck.

You will establish Kapitus's core operating framework for governing, documenting, managing, and building trust in data as a strategic enterprise asset, transforming Data Governance into measurable practices that promote accountability, transparency, defensibility, and accountable ownership across Business Data Owners, Business Data Stewards, and Technical Data Custodians.

Your work will support data modernization, BI, analytics, Finance, Risk, regulatory reporting, and responsible AI by ensuring enterprise data is reliable, understood, traceable, and appropriately managed, while helping shape the Enterprise Data Governance program's future capabilities in Data Quality, stewardship, lifecycle assurance, governance controls, and Model and AI Governance.

What You Will Do:

Governance Framework and Operating Model

• Architect and operationalize Kapitus's Enterprise Data Governance framework to support a practical, evidence-based, defensible, and audit-ready operating model.
• Develop and maintain governance standards, procedures, decision rights, intake processes, escalation paths, and operating cadences.
• Translate governance policy and standards into measurable, repeatable practices embedded in data delivery, analytics, reporting, and business operations.
• Establish governance reporting, decision records, issue tracking, exception processes, and evidence practices that support accountability and continuous improvement.
• Support governance alignment with applicable internal policy, risk, compliance, and regulatory expectations.

Stewardship and Domain Enablement

• Design and operationalize the enterprise data stewardship model for Business Data Owners, Business Data Stewards, and Technical Data Custodians.
• Define stewardship roles, responsibilities, accountabilities, and practical ways of working across priority business domains.
• Develop stewardship playbooks, guidance, training, and working sessions that enable Data Owners and Stewards to fulfill their governance responsibilities.
• Facilitate domain-level governance activities, including standards reviews, issue escalation, data-quality reviews, certification activities, and stewardship forums.
• Support the VP, Data Governance & BI in preparing materials and tracking outcomes for the Enterprise Data Governance Council and associated governance bodies.

Metadata, Glossary, Critical Data Elements, and Lineage

• Lead the design and ongoing curation of the Enterprise Business Glossary in Atlan.
• Establish governance processes for business-term definition, review, approval, publication, and periodic maintenance.
• Support the identification, prioritization, ownership, and stewardship of Critical Data Elements across enterprise data domains.
• Define metadata, documentation, and lineage standards that create traceability from foundational data elements through curated datasets, semantic models, KPIs, reports, and regulatory metrics.
• Partner with business and technical teams to ensure business meaning, technical metadata, and data lineage are complete, consistent, and usable.

Data Quality and Issue Remediation

• Define and establish the enterprise Data Quality framework, including quality dimensions, business rules, thresholds, profiling, assessment, monitoring, issue-management, and remediation practices.
• Partner with Business Data Stewards and Technical Data Custodians to establish Data Quality ownership, define meaningful rules, investigate issues, and drive root cause analysis and remediation at the source.
• Establish repeatable Data Quality assessment and reporting processes.
• Maintain a living Data Quality risk register that documents material findings, business impact, accountable owners, remediation actions, and status.
• Partner with the Data Quality Lead as the capability matures to operationalize and continuously improve Data Quality monitoring, controls, reporting, and remediation.

Data Classification, Retention, and Lifecycle Governance

• Establish enterprise standards and governance processes for data classification, sensitivity, criticality, retention, archiving, and disposal.
• Lead development of the Enterprise Data Retention Standard and associated governance practices, in partnership with Legal, Compliance, Information Security, Risk, and business stakeholders.
• Enable Business Data Owners, Business Data Stewards, and Technical Data Custodians to understand and consistently apply approved retention, archival, and disposal requirements within their data domains.
• Support the creation and maintenance of data inventory, classification, retention, and lifecycle evidence needed to demonstrate adherence to approved standards.
• Partner with relevant control functions to ensure lifecycle governance practices align with applicable legal, regulatory, risk, security, and internal policy requirements.

Governed Data and Analytical Asset Delivery

• Partner with Data Engineering, Analytics Engineering, BI, and Enterprise Architecture to define practical governance standards for governed data and analytical assets.
• Establish governance expectations for datasets, data products, dashboards, reports, semantic models, and related analytical assets, including ownership, business definitions, documentation, classification, data quality, and traceability.
• Define certification criteria and govern the certification lifecycle in partnership with Data Owners and Stewards.
• Ensure approved business definitions are consistently reflected in semantic-layer and BI implementations.
• Help embed governance requirements into Snowflake, dbt, BI, analytics, and data-delivery processes.

Atlan Functional Ownership and Technology Enablement

• Serve as the functional owner for Atlan and related governance capabilities.
• Define business requirements, governance workflows, metadata standards, stewardship processes, and automation opportunities for implementation by the Lead DG Platform Engineer.
• Partner with the Lead DG Platform Engineer on platform configuration, integrations, automation, enhancements, and operational adoption.
• Ensure governance technology supports glossary management, stewardship workflows, metadata, integrations, classification, lineage, certification, Data Quality monitoring, and evidence reporting.

Model and AI Governance Support

• Support the development of risk-based governance requirements for analytical models, machine learning, generative AI, AI-enabled workflows, and applicable third-party AI services.
• Partner with Risk, Compliance, Legal, Information Security, Product, Operations, and relevant technical teams to support model and AI-use-case inventory, ownership, documentation, data-use review, lineage, monitoring, and lifecycle controls.
• Help promote the responsible use of governed enterprise data across reporting, analytics, machine learning, and AI initiatives.
• Partner with a future Model & AI Governance Lead as the capability matures and organizational demand becomes clearer.

What We're Looking For:

• 10+ years of experience in Data Governance, Data Management, Data Quality, or related data disciplines.
• Demonstrated success building, implementing, or significantly maturing enterprise Data Governance capabilities in a modern data environment.
• Experience developing stewardship programs, governance operating models, policies, standards, workflows, and governance forums.
• Experience working in a regulated, risk-sensitive, or audit-focused environment is strongly preferred.
• Expert-level experience with business glossary design, metadata management, data cataloging, data lineage, and governance operating processes; Atlan experience is preferred.
• Demonstrated experience designing and enabling federated data stewardship models involving business owners, stewards, and technical custodians.
• Strong knowledge of Data Quality frameworks, business-rule definition, profiling, monitoring, issue management, root cause analysis, and remediation.
• Experience establishing or applying data classification, sensitivity, criticality, retention, archival, disposal, lifecycle-management, and certification practices.
• Familiarity with DAMA DMBOK2 or similar Data Management frameworks, with the judgment to apply them pragmatically.
• Solid understanding of modern data and analytics environments, including Snowflake