Data Governance Analyst

Hi,

Hope you are doing well.

Please find the below JD and let me know your interest at your earliest convenience.

In this role, you will help advance SCAN s AI enablement and enterprise data governance practices. This role is responsible for improving the trust, usability, and business context richness of data that powers AI, analytics, reporting, and operational decision-making.
This role helps build the foundation for enterprise AI by strengthening core knowledge management capabilities via the development, governance, and adoption of enterprise data assets and knowledge structures, including business glossaries, governed metrics, semantic layers, ontologies, knowledge graphs, metadata, lineage, and stewardship practices. You will work closely with data stewards, subject matter experts, platform teams, and executive business leaders to build context, standardize business terminology, improve data quality, and make enterprise data easier for people and AI-enabled tools to understand and use.
This is an essential role as SCAN modernizes its data ecosystem through a modern platform, scalable self-service analytics, and AI-assisted ways of working.

You Will
Enhance SCAN s semantic foundation by developing dimensional models, hierarchies, domain relationships, and governed metrics that enable consistent AI use cases.
Support ontology and knowledge graph initiatives by identifying key business entities, relationships, synonyms, and domain concepts that improve data connectivity, searchability, and AI comprehension.
Govern and document data assets across Snowflake, Databricks, and Iceberg environments to ensure structured and semi-structured data is well-described, discoverable, and trusted for analytics and AI workflows.
Maintain and enrich the enterprise data catalog, documenting metadata, lineage, ownership, usage context, and business meaning for high-value data assets.
Improve data discoverability and usability through tagging, glossary management, workflow optimization, and metadata quality controls.
Perform data and metric validation to assess accuracy, completeness, aggregation logic, consistency, and timeliness across data products, including via the use of AI-assisted tools.
Partner with business stakeholders, data stewards, subject matter experts, analytics teams, and technical teams to execute enterprise data governance and AI-readiness priorities across key domains.
Investigate data quality issues, conduct root cause analysis, document business impact, and partner with data engineering, analytics, reporting, and business teams to support resolution.
Participate in Agile ceremonies to ensure governance and AI enablement work is visible, prioritized, and delivered effectively.
Drive continuous improvement in governance automation, stewardship workflows, reporting certification, and AI-assisted governance capabilities.
We seek Rebels who are curious about AI and its power to transform how we operate, improve decision-making, and serve our members.
Actively support the achievement of SCAN s Vision and Goals.
Other duties as assigned.
Your qualifications
Bachelor's degree in information systems, Data Analytics, Computer Science, Business Administration, Healthcare Administration, Statistics, or a related field; equivalent experience may be considered.
3+ years of experience in data engineering, data governance, data management, data quality, analytics, business intelligence, metadata management, knowledge management, or related data-focused role.
Experience in the health care industry, especially in areas such as Medicare Advantage, clinical analytics, medical economics, quality, pharmacy, claims, provider, member, or operational data environments, is strongly preferred.
Experience with modern knowledge management techniques such as semantic layers, ontologies, knowledge graphs, retrieval-augmented generation, AI-assisted documentation, or metadata-driven discovery preferred.
Familiarity with modern warehouse and open Lakehouse concepts, including cloud object storage, open table formats such as Apache Iceberg or Delta Lake, metadata catalogs, data sharing, lineage, and governance of structured and semi-structured data.
Familiarity with using AI-assisted tools to improve documentation, analysis, data discovery, metadata quality, and governance workflows.
Experience with enterprise data catalog, metadata management, data lineage, business glossary, stewardship, and/or or report certification practices.
Experience with BI and reporting platforms such as Power BI, Tableau, SSRS, or similar tools.
Experience with CLI tools such as Cortex Code, Claude Code, or similar.
Experience working in SAFe Agile or another Agile delivery framework.
Preferred certifications include CDMP, DGSP, DCAM, DAMA-related training, Snowflake certification, Microsoft Power BI certification, or relevant Agile/SAFe certifications.
Competencies
Knowledge Management Mindset: Helps organize business meaning, definitions, relationships, ownership, and metadata so that enterprise data is easier to find, understand, govern, and reuse.
Business Partnership: Builds trusted relationships with business and technical stakeholders and translates governance, data quality, and AI-readiness practices into business value.
Analytical Thinking: Uses structured analysis to evaluate data issues, identify root causes, assess business impact, and improve trust in data used for analytics and AI-enabled capabilities.
Governance Discipline: Applies consistent standards for definitions, metadata, quality rules, stewardship, lineage, and documentation.
Communication: Explains complex data concepts clearly to both technical and non-technical audiences.
Continuous Improvement: Identifies opportunities to improve governance workflows, data usability, automation, AI-assisted documentation and scalable self-service analytics.
Skills & Abilities
Ability to support semantic layer and metrics governance, including standardized definitions, dimensional concepts, hierarchies, measure consistency, and policy adherence.
Familiarity with knowledge management concepts such as ontologies, knowledge graphs, taxonomies, and domain models.
Ability to translate technical data structures into clear, business-aligned definitions and documentation, especially via data dictionaries, catalog entries, report inventories, data quality findings, and usage context.
Strong SQL skills for data profiling, validation, reconciliation, and issue investigation.
Familiarity with Snowflake, Alteryx, SQL Server, Power BI, Tableau, SSRS, data catalogs, modern analytics platforms, and open lakehouse concepts such as cloud object storage, open table formats, metadata catalogs, and governed data access.
Ability to work effectively across business, analytics, data engineering, platform, stewardship, and leadership teams.
Practical understanding of healthcare data domains such as claims, membership, providers, clinical operations, pharmacy, quality, utilization management, or finance preferred.
Ability to manage multiple priorities in an Agile environment while maintaining attention to detail.
Awareness of emerging automation, AI-assisted documentation, data quality monitoring, semantic search, and governed by self-service analytics capabilities.