Data Governance Job Market Trends

The framework of policies and practices that guide the ethical and effective use of AI technologies within an organization.

Ensures the safe development and deployment of AI systems, addressing ethical concerns and potential risks.

A professional responsible for managing the relocation of data, systems, or applications from one environment to another. Ensures the process is seamless, efficient, and minimizes downtime or data loss.

A data architecture combining features of data lakes and data warehouses to support both analytical and transactional workloads. Allows for efficient multi-modal data processing and storage with unified governance.

Responsible for moving data between systems, ensuring accuracy and integrity during the process. Key to system upgrades and integrations within organizations.

Machine Learning Operations (MLops) involves the practices and tools used to deploy, monitor, and manage machine learning models in production.

Responsible for managing and optimizing data flow across different systems and platforms. Ensures seamless data connectivity, enhancing overall data accessibility and organizational efficiency.

Designs and implements data solutions to meet business needs, ensuring data is accessible, reliable, and secure.

Focused on the seamless transfer of data between different storage types and formats, Data Migration is crucial for maintaining data integrity and ensuring minimal operational downtime.