Principal Data Lead

Unissant, Inc. delivers innovative capabilities to the agencies that keep our nation healthy and safe. We apply our domain expertise, data acumen, and technology know-how to achieve breakthrough results for our clients. Working collaboratively, we advance missions and careers through a focus on honesty, integrity, and dependability. We continuously look for talent excited to join that effort. To learn more about our exciting organization, please visit us at

We are seeking a Principal Data Lead to join our team and support our government client on a large, multi-year IT services program.

The Principal Data Lead is accountable for data strategy execution, data supply chain and metadata maturity leadership, trusted-data promotion governance, data quality standards, and data-domain coordination across MACBIS source systems and CMS data owners. This role will work closely with government data owners, program leadership, architects, product leaders, engineering teams, analytics and AI/ML teams, cybersecurity personnel, and source-system stakeholders to ensure program data is discoverable, trusted, governed, secure, interoperable, high quality, and fit for mission use.

The ideal candidate is a strategic, hands-on data leader with demonstrated experience serving as a Principal Data Lead, Chief Data Officer, Data Strategy Lead, Enterprise Data Architect, or comparable senior data leadership role on a large federal IT services program. They bring deep expertise in enterprise data strategy, data governance, data management, metadata management, data quality, data integration, data lineage, data catalogs, data stewardship, data-sharing practices, and cross-organization data coordination.

• Lead execution of the program's data strategy in alignment with government mission priorities, enterprise data objectives, architecture, security and privacy requirements, analytics needs, AI-enabled services, and operational requirements.
• Establish, operate, and continuously improve the program's data supply chain approach, including data sourcing, ingestion, transformation, validation, storage, access, distribution, consumption, monitoring, and retirement.
• Lead metadata maturity activities, including the development and maintenance of business, technical, operational, and governance metadata; data catalogs; data dictionaries; data classifications; lineage; ownership; stewardship; and data-product documentation.
• Establish and oversee trusted-data promotion governance to ensure data sets, data products, analytics assets, and shared information are assessed, approved, documented, monitored, and made available according to defined quality, security, privacy, stewardship, and usability standards.
• Define, document, communicate, and govern data quality standards, data-quality rules, quality measurements, issue-management processes, remediation practices, and continuous-improvement activities.
• Partner with MACBIS source-system stakeholders and CMS data owners to coordinate data-domain priorities, ownership, stewardship, access, quality, metadata, interoperability, and data-sharing decisions.
• Facilitate data-governance forums, data-domain working groups, stewardship meetings, and decision-making processes involving government data owners, source-system teams, technical teams, and program leadership.
• Define and maintain data-domain models, data ownership structures, stewardship roles, critical data elements, business glossaries, data dictionaries, and data-management policies and procedures.
• Ensure data solutions support secure, appropriate, and auditable access; data privacy; data protection; data retention; data classification; data-use controls; and applicable government requirements.
• Collaborate with architects, engineers, cloud teams, cybersecurity professionals, product leaders, analytics teams, AI/ML teams, and operations personnel to implement approved data standards, governance controls, integration patterns, and data-management practices.
• Oversee data lineage, provenance, traceability, and auditability practices to support confidence in data used for operational, reporting, analytics, and AI-enabled use cases.
• Identify, assess, document, and manage data risks, data dependencies, data-quality issues, metadata gaps, integration challenges, governance gaps, and cross-domain decision points.
• Develop and maintain data strategy and governance artifacts, including data roadmaps, data-domain models, data catalogs, metadata…