* 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).

Knowledge of the capabilities and functionality associated with various technologies for organizing and managing information (e.g., databases, bookmarking engines).

Knowledge of the characteristics of physical and virtual data storage media.

Skill in developing data models.

Analyze and plan for anticipated changes in data capacity requirements.

Analyze information to determine, recommend, and plan the development of a new application or modification of an existing application.

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

Provide recommendations on new database technologies and architectures.

* 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.

Confer with systems analysts, engineers, programmers and others to design application.

Advise higher level leadership on critical data management issues.

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.

Examine and identify database structural necessities by evaluating operations, applications, and programming.

Prepare database design and architecture reports.

Effectively allocate storage capacity in the design of data management systems.

* Knowledge of specific operational impacts of cybersecurity lapses.

* 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 operations (DataOps) processes and best practices.

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

Skill in designing the best approach and architecture for automated data labeling and data lifecycle.

Knowledge of data administration and data standardization policies and standards.

Knowledge of network access, identity, and access management (e.g., public key infrastructure [PKI]).

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

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

Knowledge of emerging security issues, risks, and vulnerabilities.

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

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

Knowledge of service management concepts for networks and related standards (e.g., Information Technology Infrastructure Library, current version [ITIL]).

Knowledge of an organization’s information classification program and procedures for information compromise.

Maintain current knowledge of advancements in DoD AI Ethical Principles and Responsible AI.

Skill in developing machine understandable semantic ontologies.

Knowledge of container orchestration and resource management platforms.

Knowledge of DoD AI Ethical Principles (e.g., responsible, equitable, traceable, reliable, and governable).

Knowledge of how AI solutions integrate with cloud or other IT infrastructure.

Knowledge of how commercial and federal solutions solve Defense-related data environment and platform challenges.

Knowledge of how to automate development, testing, security, and deployment of AI/machine learning-enabled software to the DoD.

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