Senior Data Management and Governance Lead

Seeking a Senior Data Management Consultant to support a Deputy Assistant Secretary of the Navy - Procurement (DASN(P)) contract

The ideal candidate must have demonstrated performance and proven success contributing to enterprise-wide data governance programs in complex environments. The candidate must possess comprehensive knowledge of data quality assurance, metadata management, data architecture, and data lineage.

The candidate must be proficient with using visualization tools, and the data analysis process to provide insights for executive level decision making. The candidate must have executive level communication skills, including ability to provide compelling presentations to senior leadership.

The candidate must also be able to perform the following:

• Strong stakeholder management with proven ability to build relationships and drive cross-functional alignment.
• Ability to translate technical concepts into actionable business language for non-technical audiences.
• Experience facilitating committees, working groups, and collaborative forums.
• Strategic thinker with strong analytical and problem-solving skills.
• Self-directed with ability to work independently while appropriately escalating issues.
• Strong organizational skills and the ability to manage multiple priorities.
• Collaborative team player across organizational boundaries.

• Data Strategy & Governance Documentation:
  Within 180 days of award, the candidate shall analyze Government requirements and existing data structures to deliver a draft Department of Navy (DON) Procurement Data Strategy and a corresponding Data Governance Plan. These documents shall be actionable technical frameworks that produce and maintain the following foundational artifacts that will define and enforce the rules for managing the DON's procurement data as a strategic asset.
• Data Stewardship Framework:
  Defines data stewardship roles, technical responsibilities, and data access control configurations. The data stewardship framework shall include a Governance Activity Responsible, Accountable, Consulted, and Informed (RACI) Matrix, which will be used at every step in the data lifecycle. This framework shall include the chartering and operational procedures for all individuals involved with the usage of the data. In addition, the framework will outline all control access procedures.
• Data Quality Framework:
  Defines automated data quality validation rules and the (e.g., Accuracy, Completeness, Timeliness, Uniqueness, Consistency), and thresholds used to measure data integrity.
• Conceptual Data Model: A high-level, business-focused model, developed in collaboration with Government stakeholders, that defines the primary business entities (e.g., "Contract," "Vendor," "Requirement") and their fundamental relationships, serving as the strategic blueprint for all subsequent data models.
• Canonical Data Model and Master Data Dictionary: A detailed logical data model that serves as the master data dictionary. This model must be visually represented through a comprehensive Entity Relationship Diagram (ERD) that maps the relationships between key data entities across all integrated source systems. For every data element, this model must record, at a minimum, the following metadata attributes:
  • Authoritative Source System
  • Business Process Alignment (BEA L1-L4)
  • Designated Government Data Owner
  • Data Flow Diagrams (DFDs) and Lineage: A set of diagrams and supporting documentation that illustrate the end-to-end lineage of data. This includes visualizing the movement of data between source systems, through key business processes (such as the Procure-to-Pay handshakes), and into the enterprise data platform's storage layers, identifying all inputs, outputs, and transformations. Documentation shall support automated metadata propagation, ensuring that data sensitivity classifications and quality assurance flags are applied to all data products.
• Enterprise Data Catalog:
  An enterprise-accessible catalog populated with all the artifacts and metadata defined above (including the Conceptual Model, ERD, and DFDs) to ensure data assets are discoverable, understandable, and trustworthy.
• Data Literacy and Culture Program:
  This includes the creation of reference guides, and training material for data stewards and product owners. The program shall focus on shifting organizational perception of data as a compliance burden to a strategic business enabler and shall use measurable benchmarks to track the workforce’s ability to find, understand, and use data.

• Collaborate on design, and develop and execute comprehensive data governance frameworks, policies, and operating procedures aligned with organizational objectives and regulatory requirements. These documents will be version controlled deliverables which will be retained in approved DASN(P) locations.
• Partner with the senior management to align data governance initiatives with enterprise goals and industry best…