* Knowledge of computer networking concepts and protocols, and network security methodologies.

Knowledge of data administration and data standardization policies and standards.

* Knowledge of risk management processes (e.g., methods for assessing and mitigating risk).

Skill in developing data dictionaries.

Implement data management standards, requirements, and specifications.

Analyze and define data requirements and specifications.

Manage the compilation, cataloging, caching, distribution, and retrieval of data.

Ability to prepare and deliver education and awareness briefings to ensure that systems, network, and data users are aware of and adhere to systems security policies and procedures.

Knowledge of Personally Identifiable Information (PII) data security standards.

Knowledge of Personal Health Information (PHI) data security standards.

* Knowledge of national and international laws, regulations, policies, and ethics as they relate to cybersecurity.

* Knowledge of cybersecurity principles.

* Knowledge of cyber threats and vulnerabilities.

Assess the validity of source data and subsequent findings.

Review feedback on customer satisfaction and internal service performance to foster continual improvement.

Assist integrated project teams to identify, curate, and manage data.

Collaborate with appropriate personnel to address Personal Health Information (PHI), Personally Identifiable Information (PII), and other data privacy and data resusability concerns for AI solutions.

Collaborate with data owners to establish data quality rules and definitions.

Create data catalogs and dictionaries.

Create metrics that characterize the usability, timeliness, completeness, and accuracy of data for multiple users to reference and use.

Identify and document customer requirements when on-boarding new data assets.

Recommend data collection, integration, and retention requirements.

Ability to collect, verify, and validate test data.

* Knowledge of specific operational impacts of cybersecurity lapses.

Skill in communicating with all levels of the organization, including senior/mid-level executives, and operational-level personnel (e.g., interpersonal skills, approachability, effective listening skills, appropriate use of style and language for the audience).

* Knowledge of cloud computing service models Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS).

* Knowledge of cloud computing deployment models in private, public, and hybrid environment and the difference between on-premises and off-premises environments.

Knowledge of data protection standards and frameworks to prevent unauthorized access to data, and safeguard against unauthorized disclosure of data.

Knowledge of data security roles and responsibilities.

Knowledge of how to collect, store, and monitor data.

Knowledge of laws, regulations, and policies related to AI, data security/privacy, and use of publicly procured data for government.

Knowledge of Personal Health Information (PHI), Personally Identifiable Information (PII), and other data privacy and data reusability considerations for AI solutions.

Skill in labeling data to make it more discoverable and understandable.

Knowledge of how information needs and collection requirements are translated, tracked, and prioritized across the extended enterprise.

Consult with customers and key stakeholders to evaluate functional requirements for AI and data applications.

Knowledge of the organization’s core business/mission processes.

Skill in developing machine understandable semantic ontologies.

Knowledge of key decision-support needs and questions to drive prioritization of data efforts.