Director, Data Governance

About the role

The Director, Data Governance, in the Global Analytics & Insights (GAI) organization, owns the governance function for Avalara’s enterprise data, metrics, and platforms - establishing the policies, controls, and practices that make GAI’s data trusted, secure, and fully compliant. This is a founding role for this function: you’ll take over the current processes, identify areas for improvement, gaps, and other opportunities, define a roadmap and scale into a full practice as the function matures.

Sits inside the GAI Data & Analytics Engineering organization and operates as a player-coach in the near term. Partners with Internal Audit and Information Technology to ensure the systems are complete and accurate.

Responsibilities

• Function Leadership: Assess the current state of data governance processes, tooling, and practices within GAI — identify gaps, risks, and opportunities, and develop a prioritized roadmap to address them.

• Own and evolve the Data Governance function within GAI — setting the operating model, quality bar, and strategic direction for how GAI's data assets are classified, accessed, and stewarded.

• Review all processes, systems, and protocol for work related to critical data, partnering with engineering to ensure processes evolve to meet the highest standards.

• Maintain and mature the existing role-based access governance model for GAI — including access controls, request/review processes, periodic access reviews, and data sensitivity audits across Snowflake and GAI's data platforms.

• Lead the classification of GAI's data assets against Avalara's enterprise taxonomy (Public, Internal Use, Sensitive, Restricted); drive remediation of unclassified or misclassified data.

• Partner with Legal, IT, and Privacy to ensure GAI's data handling practices comply with applicable regulatory and contractual requirements, including SOC 2.

• Build and maintain a data quality framework for GAI — quality dimensions, monitoring, alerting, and remediation processes; apply particular rigor to financial metrics and reporting data given their sensitivity and downstream impact.

• Champion development of a data catalog and metadata management capability within GAI; ensure pipelines, models, and data products are discoverable, documented, and trusted.

• Establish data lineage practices that provide visibility into how data flows through GAI systems and where it is consumed downstream.

• Serve as GAI's primary liaison to Avalara's enterprise governance, security, privacy, and AI CoE teams — ensuring GAI practices align with company-wide frameworks.

• Partner with Internal Audit and Information Technology on compliance reviews, control assessments, and system-level accuracy — ensuring GAI's data systems meet enterprise audit standards.

• Engage executives and cross-functional leaders on data risk, compliance posture, and governance maturity; influence adoption without formal authority over partner functions.

• Work with Data Engineering and Analytics Engineering to ensure governance requirements are built into pipelines and semantic models, not bolted on afterward.

Qualifications

• 8+ years of experience in data management, data engineering, analytics, or a closely related field; ideally in a SaaS or technology business at scale.

• Deep expertise in data governance fundamentals — data access controls, classification, data quality management, lineage, and metadata management.

• Experience working in or alongside finance organizations — financial reporting, analytics, or financial data processes — with a strong understanding of how financial data is produced, consumed, and governed.

• Strong understanding of data security and privacy compliance requirements and how they translate to technical and operational controls.

• Proven ability to drive adoption without formal authority — able to influence engineering teams, business stakeholders, and executive audiences simultaneously.

• Experience building governance policies, data stewardship models, and process frameworks, not just inheriting them.

• Hands-on experience with Snowflake or an equivalent cloud data warehouse.

• Familiarity with dbt or similar pipeline/transformation tooling — enough to partner effectively with Data Engineering, not to build pipelines directly.

• Experience with data catalog or metadata management platforms (e.g., Alation, Collibra, DataHub, or similar).

• Experience with role-based access controls in cloud data environments, including conducting periodic access reviews, data sensitivity assessments, and compliance audits.