Senior Database Administrator

Full Position Description (In depth detail in bullet or paragraph form): Establish and maintain a comprehensive, efficient, and secure data management framework that enables the client Federal Government agency business unit to collect, store, process, and analyze data seamlessly. The functional objective is to ensure data integrity, accessibility, and security while promoting automation, modernization, and effective decision-making through the optimization of data workflows, integration, and governance practices. The purpose is to support business operations, enhance data-driven insights, and ensure compliance with relevant regulations and standards through the following 6 focus areas:

1 - Establish a Comprehensive Data Management Framework:
Conduct a detailed assessment of current data systems and workflows, identifying key areas for improvement.
Design and document a data management framework that aligns with organizational goals, addressing data collection, storage, processing, and analysis needs.
Develop a roadmap for implementing the data management framework, ensuring that all necessary tools and technologies are in place for seamless operation.
Set up and maintain a central data repository or data warehouse to store collected data from various sources for easy access and management.

2 - Data Integrity
Implement data validation rules and automated data cleansing processes to ensure data accuracy and integrity.
Develop access control policies to restrict and monitor data access based on user roles and permissions, ensuring the principle of least privilege.
Establish data backup and disaster recovery protocols to safeguard data against potential loss or corruption.
Align with client agency s Data Council to oversee the implementation and maintenance of the data governance framework.

3 - Promote Automation in Data Collection, Storage, Processing, and Analysis:
Implement automated data collection pipelines to streamline the gathering of data from various internal and external sources (e.g., APIs, databases, IoT devices).
Develop automated workflows for data processing, including data transformation, cleansing, and validation, to reduce manual intervention and errors.
Integrate automated reporting tools to generate real-time or scheduled reports with minimal manual effort.
Set up automated data analysis tools (e.g., machine learning models, AI algorithms) to extract actionable insights from data with minimal manual processing.

4 - Creation and Maintenance of Data
Regularly update the data dictionary with new data elements and changes to existing ones as systems and processes evolve. This includes adding new data sources, fields, and their descriptions when integrating new data systems or features.
Track historical changes to the data dictionary to maintain version control.

5 - Naming and Standards
Define and enforce standardized naming conventions for datasets, fields, and variables to ensure consistency.
Ensure that data definitions align with industry best practices and organizational standards.

6 - Accessibility
Ensure the report data is easily accessible to all stakeholders, including data analysts, IT personnel, and business users, through a user-friendly interface or central repository.
Provide search functionality and filters to help users locate relevant data elements and metadata quickly.
Document clear instructions on how to use and contribute to the data report.