Data Governance Lead

Role & Responsibilities Overview:

• Design and develop enterprise-wide data governance programs across on-premises and cloud data estates, incorporating insurance-domain best practices for policy, claims, underwriting, billing, finance, and regulatory reporting data.
• Define governance operating models, ownership structures, stewardship roles, data councils, and escalation mechanisms to improve accountability across business, data product, technology, and analytics teams.
• Develop policies and processes for data collection, storage, access, sharing, usage, quality, archival, privacy, security, technology, tooling, modeling, reporting, and visualization across insurance data assets.
• Establish insurance-specific data standards, naming conventions, business glossaries, data dictionaries, reference data definitions, and canonical data models to drive consistency across policy, claims, customer, producer, product, and coverage datasets.
• Define and implement data quality frameworks covering profiling, validation rules, completeness, accuracy, consistency, timeliness, uniqueness, exception management, issue tracking, root-cause analysis, and remediation workflows.
• Partner with domain owners, data product teams, actuarial, underwriting, claims, operations, compliance, and analytics stakeholders to operationalize data quality controls and improve trust in insurance reporting and decision-making datasets.
• Leverage Azure cloud technologies such as Microsoft Purview, Azure Databricks, Unity Catalog, Azure Data Lake, Azure Synapse, Azure Data Factory, and Azure SQL to enable metadata management, lineage tracking, access control, cataloging, and audit readiness.
• Use Collibra, Ataccama, Informatica Axon/EDC, Alation, or similar governance and data quality platforms to manage business glossaries, stewardship workflows, policy compliance, data quality scorecards, lineage, and governance KPIs.
• Support sensitive data classification and controls for PII, PHI, PCI, financial, and confidential insurance data, ensuring alignment with privacy, security, retention, and compliance requirements.
• Help the Data Governance Committee define governance policies, standards, metrics, adoption roadmaps, compliance checkpoints, and change management processes for enterprise data governance initiatives.
• Lead teams responsible for MDM implementation, data quality engineering, governance analysis, metadata management, data stewardship, and other governance-related delivery activities.

Candidate Profile:

• 8-12 years of experience in designing, implementing, and operationalizing enterprise data governance frameworks across on-premises, cloud, and hybrid data ecosystems.
• Strong experience in the insurance domain is required, with working knowledge of policy, claims, underwriting, billing, customer, producer, product, coverage and finance, and regulatory reporting data.
• Hands-on experience defining data governance operating models, stewardship responsibilities, ownership structures, data councils, governance workflows, and issue escalation processes.
• Experience establishing insurance-specific data standards, naming conventions, business glossaries, data dictionaries, reference data definitions, canonical data models, and data standardization practices.
• Strong understanding of data quality management, including data profiling, rule definition, validation, completeness, accuracy, consistency, timeliness, uniqueness, exception handling, root-cause analysis, remediation tracking, and quality scorecards.
• Hands-on knowledge of Azure cloud data governance technologies such as Microsoft Purview, Azure Databricks, Unity Catalog, Azure Data Lake, Azure Synapse, Azure Data Factory, and Azure SQL for metadata management, lineage, cataloging, access control, and audit readiness.
• Experience with data governance and data quality platforms such as Collibra, Ataccama, Informatica Axon/EDC, Alation, or similar tools for business glossary management, stewardship workflows, policy compliance, data quality monitoring, and governance KPI reporting.
• Knowledge of sensitive data classification and controls for PII, PHI, PCI, financial, and confidential insurance data, with understanding of privacy, security, retention, and compliance requirements.
• Ability to partner with business, technology, analytics, actuarial, underwriting, claims, compliance, and data product stakeholders to drive governance adoption and improve trust in enterprise data assets.
• Based out of the US, with the ability to work closely with client stakeholders across business and technology teams.