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

Knowledge of data administration and data standardization policies and standards.

Knowledge of data mining and data warehousing principles.

Knowledge of database management systems, query languages, table relationships, and views.

Knowledge of query languages such as SQL (structured query language).

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

Skill in establishing data security controls.

Skill in developing data dictionaries.

Implement data management standards, requirements, and specifications.

Develop and implement data mining and data warehousing programs.

Develop secure code and error handling.

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

Perform secure programming and identify potential flaws in codes to mitigate vulnerabilities.

Record and manage test data.

Knowledge of Java-based database access application programming interface (API) (e.g., Java Database Connectivity [JDBC]).

Knowledge of database access application programming interfaces (APIs) (e.g., Java Database Connectivity [JDBC]).

* 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.

Skill in data mining techniques (e.g., searching file systems) and analysis.

Program custom algorithms.

Advise higher level leadership on critical data management issues.

Apply data acquisition, cleaning, transformation, and ingestion best practices for machine learning data conduits.

Assess and address the limitations of methods to deliver data.

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

Build automated data management conduits.

Comply with data classification and handling requirements through access control and security best practices.

Manipulate and clean large, disparate datasets for bulk analysis to identify connections.

Ability to collect, verify, and validate test data.

Knowledge of how to utilize Hadoop, Java, Python, SQL, Hive, and PIG to explore data.

Read, interpret, write, modify, and execute simple scripts (e.g., PERL, VBS) on Windows and UNIX systems (e.g., those that perform tasks such as: parsing large data files, automating manual tasks, and fetching/processing remote data).

Skill in data pre-processing (e.g., imputation, dimensionality reduction, normalization, transformation, extraction, filtering, smoothing).

Skill in performing format conversions to create a standard representation of the data.

Skill in transformation analytics (e.g., aggregation, enrichment, processing).

Skill in using data mapping tools.

Skill in writing scripts using R, Python, PIG, HIVE, SQL, etc.

* 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 data security roles and responsibilities.

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

Skill in developing and maintaining automation scripts.

Skill in identifying data acquisition, collection, and curation risks.

Implement data mining and data warehousing applications.

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

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

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

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.

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

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 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.