Director of Data Governance & Data Quality

Vertex Education•Chandler, AZ

About The Position

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!

Requirements

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.

Responsibilities

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.
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.
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.
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.
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.