We are looking for an experienced Data Governance Lead to drive data governance strategy and execution for our enterprise Snowflake-based data platform. This role sits at the intersection of data modeling, data quality, security, and compliance — ensuring that data flowing through our Snowflake environment is trusted, well-documented, secure, and used responsibly across the organization.
You will work closely with data architects, data modelers, data engineers, and business stakeholders to establish governance frameworks, enforce standards on the Snowflake data model (raw, staging, curated/consumption layers), and enable self-service analytics without compromising data integrity or security.
Key Responsibilities
- Define and implement the data governance framework (policies, standards, roles, and workflows) for the Snowflake data platform.
- Partner with data modelers/architects to enforce governance standards across dimensional/Data Vault/medallion (bronze-silver-gold) layers — naming conventions, metadata standards, and modeling best practices.
- Own data quality initiatives: define quality rules, implement monitoring/alerting (e.g., via Snowflake native tools, dbt tests, Great Expectations, or Monte Carlo), and drive remediation with data owners.
- Establish and maintain a data catalog and business glossary, ensuring lineage, metadata, and definitions are accurate and discoverable (e.g., using Snowflake's native catalog, Alation, Collibra, Atlan, or Purview).
- Design and enforce access control and data security policies in Snowflake — RBAC, row-level security, dynamic data masking, tagging, and object-level permissions — in coordination with InfoSec.
- Drive data classification (PII, PHI, confidential, public) and ensure compliance with relevant regulations (GDPR, CCPA, HIPAA, SOX, etc.) within Snowflake.
- Define and monitor data lineage across ingestion, transformation (ETL/ELT via dbt, Matillion, ADF, etc.), and consumption layers.
- Establish data stewardship roles and RACI across business and technical teams; run governance councils/forums.
- Collaborate with data engineering teams to embed governance checks into CI/CD pipelines for Snowflake objects and dbt models.
- Track and report governance KPIs: data quality scores, policy compliance, catalog coverage, incident resolution time.
- Support audits and regulatory reporting by ensuring traceability and documentation of data policies and controls.
- Provide guidance and training to data teams and business users on governance best practices.
Required Skills & Experience
- 14+ years in data management, with 5+ years focused on data governance, and hands-on experience with Snowflake in a production environment.
- Strong understanding of data modeling concepts (Kimball/dimensional modeling, Data Vault 2.0, or medallion architecture) as applied within Snowflake.
- Hands-on experience with data modeling tools such as erwin Data Modeler, ER/Studio, SqlDBM, dbt (for modeling/documentation), Lucidchart/Vertabelo, or similar — used to design, document, and govern Snowflake schemas.
- Working knowledge of SQL and Snowflake-specific features: RBAC, secure views, dynamic data masking, row access policies, tagging, Time Travel, and Snowflake's information schema/ACCOUNT_USAGE views.
- Experience with data catalog / governance tools (e.g., Collibra, Alation, Atlan, Microsoft Purview, Snowflake Horizon).
- Experience with dbt or similar transformation frameworks, including documentation and testing features.
- Familiarity with regulatory and compliance frameworks (GDPR, CCPA, HIPAA, SOC 2, etc.).
- Understanding of data lineage and metadata management concepts and tooling.
- Excellent stakeholder management skills — ability to work across business, engineering, legal/compliance, and security teams.
- Strong documentation and communication skills; comfortable presenting to senior leadership.
Preferred / Nice to Have
- Certifications: SnowPro Core / Advanced, CDMP (Certified Data Management Professional), or DAMA DMBOK familiarity.
- Experience with cloud platforms (AWS/Azure/GCP) and orchestration tools (Airflow, ADF).
- Exposure to MDM (Master Data Management) tools and processes.
- Prior experience setting up a data governance function from the ground up.
- Knowledge of scripting (Python) for automation of governance/quality checks.
What Success Looks Like
- A documented, adopted data governance framework with clear ownership across the Snowflake platform.
- Measurable improvement in data quality scores and reduction in data incidents.
- High catalog/glossary coverage with accurate lineage for critical data assets.
- Access and masking policies aligned with data classification, with zero critical audit findings.
- Strong stakeholder trust in the data platform, reflected in increased self-service adoption.