Lead Data Governance Architect

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 Kapituss 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 programs 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 Kapituss 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 Were 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 dbt Tableau Power BI and Sigma.

• Understanding of how governance integrates with data pipelines data modeling semantic layers analytics reporting metadata platforms and automated lineage.

• Ability to define clear functional requirements for governance platforms workflows and automation.

• Demonstrated ability to translate governance policy and standards into practical repeatable processes adopted by business and technical teams.

• Ability to operate as a Player Architect balancing strategic framework design with hands-on delivery.

• Experience facilitating stewardship workshops stakeholder working sessions standards development and governance decision-making.

• Exceptional communication and influencing skills with the ability to translate governance concepts into clear business value and practical action.
• CDMP certification is preferred.
• Experience working in the financial industry is strongly preferred.

Kapitus Total Rewards Package Includes:

• Competitive Base Salary Rangeof $117800 to $189000. Kapitus is providing this as a good faith salary range to comply with applicable law. The applicants final salary will depend on a number of factors including the applicants geographic location skills and experience

• Annual Incentive Compensation Eligibility Up to 10% annually