Senior Data Governance Analyst

Athene Holding LTD•West Des Moines, IA

27d

About The Position

We are driven to do more. More for our customers and the financial professionals who offer our products. If you are driven to do more and love the challenge of pursuing more, Athene is your kind of company. You will find we offer more than the basics to create an inclusive and dynamic work environment at our various locations. Purpose: The Senior Data Governance Analyst is a key leader in advancing enterprise data governance, ensuring data quality, compliance, AI data readiness and advanced analytics. This role is responsible for continued maturing of the governance frameworks that enable trusted, well-documented, and ethically managed data across the organization. The Sr Analyst drives automation in metadata capture and classification, enabling scalable governance and AI-driven insights across the enterprise.

Requirements

Bachelor's degree in Data Management, Computer Science, Business, or a related field (Master's preferred).
5+ years of experience in data governance, metadata management, or enterprise data analytics roles.
Proven experience leading governance or AI data readiness initiatives in a data-driven organization.
Strong understanding of data governance frameworks (e.g., DAMA-DMBOK).
Expertise with data catalog, lineage, metadata, quality tools. Collibra DIP and Collibra DQ preferred.
Experience automating metadata ingestion and lineage using APIs, AI tools, or pattern-recognition techniques.
Knowledge of cloud data ecosystems (AWS), data warehouse (PostgreSQL) and AI data pipelines.
Excellent communication and stakeholder engagement skills.
Strategic thinker with strong analytical and problem-solving capabilities.
Comfortable leading initiatives across business, data, and technology domains.

Responsibilities

Lead the enhancement design and implementation of enterprise data governance framework, and processes to ensure data integrity, consistency, and compliance.
Drive alignment between data governance, data architecture, and AI strategy to ensure the organization's structured and unstructured data is AI-ready, well-curated, and responsibly managed.
Continued collaboration with business and technology teams to establish clear data ownership, accountability, and stewardship on new domains and stronger ties to quality along with learning the business domain knowledge.
Chair or coordinate cross-functional data governance working groups related to portfolio projects, initiatives and company objectives.
Develop and maintain data standards, documentation, and classification to support AI initiatives and the use of AI models in the market.
Assess and improve data quality, lineage, and metadata completeness to enhance deployed AI performance and reliability.
Partner with AI governance and AI engineers to identify data readiness needs specific to training data, bias detection, and traceability.
Support ethical AI practices through transparent data lineage and usage documentation.
Lead automation initiative requirements for metadata capture, lineage extraction, and data catalog population using APIs, AI/ML-based discovery, and integration tools.
Drive adoption and optimization of Collibra enterprise metadata management platform.
Ensure that metadata is continuously enriched, accurate, complete and integrated with data quality, classification, and controls.
Establish monitoring and reporting capabilities for metadata completeness and governance KPIs.
Promote and mature data quality framework with automated validation, profiling, and anomaly detection.
Partner with risk, compliance, and privacy teams to align governance with regulatory and ethical standards (e.g., NIST, NAIC, SEC).
Maintain master and reference data governance to ensure consistent definitions across systems and lines of business.
Serve as the liaison between business units, IT, and analytics teams to ensure data governance supports operational and strategic goals.
Communicate governance outcomes, data quality metrics, and AI readiness progress to executive stakeholders.
Promote a culture of data literacy, stewardship, and responsible AI use throughout the organization.
Continuously evaluate and refine governance tools, processes, and automation capabilities.
Monitor emerging trends in AI-driven data management, metadata automation, and governance technology.
Mentor analysts and stewards to strengthen the organization's data governance maturity and AI enablement.