Be the one who makes a difference!

At Vertex Education, trusted data is central to how we operate, grow, support schools, and build future data products. As the Director of Data Governance & Data Quality, you will build the operating system for data trust across a complex, multi-entity, multi-system environment. You will not be a centralized glossary writer, a BI manager, a data engineer, or a compliance service owner. You will be the leader who makes data ownership, definitions, certification, quality incidents, and source-change control unavoidable and operational.

This role requires equal parts executive diplomacy, data-product judgment, and technical fluency. You will partner with data and business system owners to ensure that critical data has clear owners, trusted definitions, documented lineage, and a governed path from source system to decision product. Your work will allow Vertex to move faster and change more lives through education!

RESPONSIBILITIES/ ESSENTIAL FUNCTIONS:

1. Governance Operating Model & Business Ownership:

• Build and run the Data Trust Council or equivalent executive cadence with clear decision rights, escalation paths, and prepared decisions for critical data domains.

• Establish and maintain the owner/steward model for enrollment, finance, student, school, and other critical domains.

• Coach business owners and stewards on their responsibilities for meaning, source-process quality, and approval of certified metrics.

• Prevent governance from becoming a centralized writing service by ensuring definitions and rules are authored and approved by the business owners closest to the work.

2. Certification, Definitions & Data Product Trust:

• Create the certification framework for data assets with a consistent user-facing meaning.

• Own the critical metric registry and definition workflow, ensuring each certified metric has a business owner, steward, source/layer authority, lineage, caveats, and change history.

• Partner with Data Engineering and BI to ensure certified datasets, dashboards, and recurring executive reports are traceable, tested, and fit for their intended decisions.

3. Data Quality Incident Management & Root-Cause Discipline:

• Design the data quality incident process for internal assets, including ownership, root-cause classification, remediation, and postmortems.

• Define data quality rules, tolerances, and monitoring expectations that Data Platform can implement in Snowflake, dbt, observability tools, or other quality systems.

• Classify recurring data failures by layer

• Route remediation to the correct owner and prevent every mismatch from defaulting to BI, Data Engineering, or executive escalation.

4. Source Change Control & Data Impact Reviews:

• Create and enforce Data Impact Reviews for material changes to critical systems and certified data models.

• Evaluate how proposed system, workflow, field, status, or integration changes affect metrics, dashboards, historical comparisons, downstream workflows, client-facing outputs, and certification status.

• Coordinate pre-go-live requirements across business owners, Technology/Application owners, Data Platform, BI, and Product where appropriate.

• Determine when an affected asset should be certified, recertified, relabeled, paused, caveated, or communicated to users based on the downstream impact of the change.

5. Cross-Functional Partnership & Education Data Context:

• Serve as the neutral operating bridge among business leaders, BI, and Data Platform.

• Establish governance standards that work across shared systems and decentralized service lines.

• Partner on student-data and client-data privacy expectations, including appropriate access, retention coordination, least-privilege use, evidence, and escalation with Security and Legal.

• Translate technical lineage, source-system complexity, and data-quality constraints into plain-language business implications for executives and non-technical leaders.

KNOWLEDGE, SKILLS & ABILITIES:

Required Qualifications:

• 7+ years of progressive experience in data governance, data quality, data strategy, analytics operations, data product operations, or related data management roles.

• Proven success creating business-owned data governance in a messy, multi-system environment where source systems, definitions, dashboards, and operational processes did not initially agree.

• Strong technical fluency with modern data environments such as Snowflake, dbt, Tableau or comparable BI tools.

• Experience designing source-change control, data quality incident management, certification standards, and owner/steward models that changed actual operating behavior.

• Ability to distinguish source-process issues, integration failures, data engineering defects, definition disputes, dashboard QA problems, timing/freshness gaps, and user-interpretation issues.