DataGalaxy included in the Gartner® Magic Quadrant™ for Metadata Management Solutions 2025
DataGalaxy included in the Gartner® Magic Quadrant™ for Metadata Management Solutions 2025
Explore executive and managerial roles that define data strategy, prioritize investments, and align data initiatives with business goals.
The Chief AI Officer is an executive responsible for defining and overseeing an organization’s AI strategy. The CAIO ensures AI initiatives align with business goals, ethical standards, and regulatory compliance — often working across data, IT, and product teams to scale AI responsibly.
A Chief Data Officer (CDO) ensures data is well-managed, trusted, and drives business value. They lead data strategy, governance, and quality, helping teams turn data into actionable insights.
The Head of Data Governance leads the implementation of policies, frameworks, and standards that ensure data quality, privacy, security, and usability across the organization. This role connects strategic business needs with operational data stewardship.
Learn about the roles responsible for enforcing policies, managing regulatory requirements, and maintaining data trust and accountability.
A Business Data Steward is responsible for defining, managing, and ensuring the proper use of data within a specific business domain. They bridge the gap between business users and technical teams by maintaining high data quality, consistency, and documentation.
A Compliance Officer ensures that the organization adheres to regulatory, legal, and internal policy requirements. In the context of data, this includes overseeing GDPR, CCPA, and industry-specific regulations like HIPAA or FERC.
The set of responsibilities and processes that ensure data and analytics assets are properly managed, defined, and used ethically and effectively across the organization. This includes business data stewards and analytics stewards who align business goals with data practices.
A Data Governance Officer oversees the strategic implementation of governance frameworks across the enterprise. This includes setting policies, driving adoption, and coordinating with data owners, stewards, and compliance teams to ensure sustainable, secure, and trustworthy data use.
A Data Owner is accountable for the overall quality, access, and usage of a specific dataset or data domain. They have authority to define data policies and delegate responsibilities to data stewards within their scope.
A data steward ensures data quality, integrity, and proper management. They uphold governance policies, maintain standards, resolve issues, and collaborate across teams to deliver accurate, consistent, and trusted data for the organization.
A Domain Owner manages a specific data domain (e.g., customer, product, finance) and is responsible for its governance, quality, and cross-team alignment. This role often overlaps with or complements that of a Data Owner in domain-oriented data organizations (like data mesh).
Get to know the analysts and specialists who turn raw data into insights, build dashboards, and support data-driven decision-making.
An Analytics Engineer builds and maintains the data transformation layer between raw data and business insights. Using tools like dbt, they design reliable, documented, and reusable data models — often serving as a bridge between data engineering and BI teams.
A Business Intelligence (BI) Analyst transforms structured data into actionable insights using reporting and visualization tools. They work closely with stakeholders to track KPIs, identify trends, and support business decision-making.
A Data Analyst collects, cleans, and interprets data to uncover trends, answer business questions, and support operational or strategic decisions. They often build dashboards, run ad hoc analyses, and work cross-functionally with data producers and consumers.
Understand the technical roles that design and maintain data pipelines, platforms, and scalable infrastructure for AI and analytics.
A Data Architect designs the structure and flow of enterprise data systems, defining how data is stored, integrated, and accessed. They create blueprints for databases, data lakes, and warehouses, aligning technical systems with governance and business requirements.
An Enterprise Architect oversees the entire IT and data ecosystem to ensure alignment with business strategy. They connect applications, infrastructure, data, and security standards across the organization, often guiding large-scale transformation initiatives.
A Platform Engineer builds and maintains the infrastructure that enables efficient data operations, often focusing on CI/CD pipelines, orchestration tools, and scalable compute environments. They ensure the data platform is robust, secure, and automation-ready.
A Solution Architect designs tailored technical solutions that solve specific business problems, typically integrating various tools, platforms, and data sources. They balance feasibility, scalability, and cost — often partnering closely with Data Architects and Engineers.
Discover the developers, stewards, and system admins who manage metadata, secure data environments, and maintain platform performance.
A Catalog Administrator manages the setup, configuration, and day-to-day operation of the enterprise data catalog. This includes overseeing user roles, metadata ingestion, and data asset curation to ensure visibility, governance, and usability across teams.
A Data Custodian is responsible for the technical management and safekeeping of data. This includes implementing security policies, controlling access, performing backups, and ensuring data availability and integrity at the system level.
A Data Engineer builds and maintains the pipelines and systems used to ingest, transform, and deliver data. They ensure scalability, performance, and reliability in the data infrastructure — enabling downstream analytics, AI, and operational use cases.
A Data Quality Analyst monitors and evaluates datasets to ensure they meet defined quality standards — including accuracy, completeness, consistency, and timeliness. They often define and implement rules using quality monitoring tools.
A Data Scientist uses statistical methods, programming, and domain knowledge to extract insights and build predictive models. They often work on advanced analytics, forecasting, and machine learning projects that inform business decisions.
A Machine Learning Engineer operationalizes ML models by building scalable training pipelines, deploying models into production, and monitoring their performance over time. They bridge the gap between data science and engineering.
A Metadata Manager oversees the governance, standardization, and curation of metadata across systems. They ensure that metadata is accurate, contextual, and accessible to support discoverability, lineage, and semantic clarity.
