Responsibilities
- Lead and grow the Data Governance function, setting the strategic direction for data ownership, stewardship policy, access governance, and data classification standards across the organization.
- Own and operate DataHub as the living catalog of the organization's data assets, covering metadata management, catalog onboarding, tagging, lineage registration, and domain stewardship.
- Extend the governance toolchain beyond DataHub by evaluating and deploying complementary platforms such as Collibra, Alation, or Atlan for policy management and business glossary; Monte Carlo or Lightup for data observability; OneTrust or Privacera for privacy and entitlement governance; and Great Expectations or dbt tests for quality rule enforcement.
- Own the enterprise Data Quality Framework, defining six quality dimensions that apply across all functions: Completeness, Accuracy, Timeliness, Consistency, Validity, and Uniqueness.
- Classify every data asset into a quality tier: Tier 1 for business critical, Tier 2 for operational, Tier 3 for exploratory. SLAs and enforcement standards are documented, visible, and enforced by tier.
- Formally document data quality SLAs per domain and per tier, publish them in DataHub, and review them on a biannual cadence.
- Define and publish a data health score methodology. Domain health scores are visible in the catalog and reported monthly to the data domain council.
- Conduct quarterly data quality audits across all functions, share findings with the domain council, and assign remediation with named accountability and due dates.
- Define and maintain the profiling policy: which datasets are profiled, how often, and what thresholds trigger an escalation. This is never left to individual interpretation.
- Govern the separation between defining quality rules and implementing them. Governance defines. Engineering and DataOps implement. When a breach occurs, Governance escalates to the owning function and tracks resolution.
- Own access control and entitlements governance, ensuring data sensitivity classifications are applied and access is granted and revoked in accordance with policy.
- Lead compliance alignment across privacy regulations, internal audit requirements, and industry standards. Partner with Legal and Security to ensure data handling practices are always current.
- Facilitate the cross functional data domain council, driving alignment across data owners, stewards, and platform teams on standards, escalations, and roadmap priorities.
- Build a culture of data accountability across the organization through training, communication, and stakeholder engagement programs.
Requirements
- Degree in Computer Science, Information Management, Business, or equivalent professional experience.
- 7 or more years of experience in data governance, data management, or a closely related field, with at least 2 years in a people or program leadership role.
- Hands on experience operating a data catalog or metadata management platform.
- Working knowledge of data observability and quality tooling, including platforms such as Monte Carlo, Lightup, Great Expectations, or dbt.
- Familiarity with privacy and entitlement governance platforms such as OneTrust, Privacera, or equivalent.
- Strong understanding of data quality frameworks, including how to define dimensions, set SLAs, build health scores, and drive remediation.
- Experience designing and enforcing data classification schemes and access governance frameworks in a cloud environment (AWS, Azure, or GCP).
- Solid understanding of regulatory and compliance requirements relevant to data, including GDPR, CCPA, SOC 2, and internal audit standards.
- Demonstrated ability to facilitate cross functional councils or governance bodies, drive alignment among opinionated stakeholders, and hold teams accountable to standards.
- Strong communication skills. You can write a policy, present findings to an executive audience, and translate data governance concepts into language that non technical stakeholders act on.
- Experience working with dbt and understanding how lineage, documentation, and testing practices connect to governance outcomes.
- Ability to build programs from the ground up and operate in an environment where governance practices are still maturing.
Nice to Have
- DAMA Certified Data Management Professional (CDMP) certification
- Experience with DataHub Cloud or Acryl Data
- Familiarity with Apache Atlas or AWS Glue Data Catalog
- Experience building data governance programs in a SaaS or B2B technology company
- Exposure to MLOps governance or AI model risk frameworks
Work Arrangement
Hybrid — Canada, Spain, Switzerland, United Kingdom, United States
Additional Information
- Flexible hybrid approach to where we work
- Candidates with disabilities who may require accommodations throughout the hiring or assessment process are encouraged to reach out to accommodations@benevity.com
