Senior Manager, Finance Data Governance

Okta•San Francisco, CA

13h•Hybrid

About The Position

Get to know Okta Okta is The World’s Identity Company. We free everyone to safely use any technology, anywhere, on any device or app. Our flexible and neutral products, Okta Platform and Auth0 Platform, provide secure access, authentication, and automation, placing identity at the core of business security and growth. At Okta, we celebrate a variety of perspectives and experiences. We are not looking for someone who checks every single box - we’re looking for lifelong learners and people who can make us better with their unique experiences. Join our team! We’re building a world where Identity belongs to you. As the Senior Manager, Finance Data Governance, you will be the architect of trust for Okta’s Finance organization. In an era where AI is redefining financial velocity, data integrity is our most valuable currency. You will sit at the intersection of cutting-edge AI innovation and rigorous financial compliance, ensuring our data is not only accessible but undeniable. You will lead the Finance Data Governance program, serving as the bridge between Finance leadership, Enterprise Data Governance, TDI, Privacy, and Legal. You'll administer the Finance Data Governance Council (FDGC), ensuring Finance implements enterprise governance standards while addressing domain specific financial data requirements. Your goal is to transform "data governance" from a compliance checkbox into a competitive advantage that accelerates AI adoption, Analytics, and executive decision-making. This is a hybrid role with an in-office requirement of two days per week at our San Francisco location.

Requirements

5+ years in Data Governance, Data Management, or Financial Systems
3+ years leadership/management experience
Finance domain familiarity (FP&A, Controllership, or financial reporting)
Communication skills to translate technical issues for executives
Experience with SaaS business metrics (ARR, Churn, Net Retention) and how they flow through systems.
Proven track record of taking a data environment from ungoverned to standardized.
Data engineering/analytics background
AI/ML governance experience
CDMP certification
SOX compliance experience

Responsibilities

Policy Creation & Strategy Provide strategic direction, oversight, and accountability for the Finance organization's data governance efforts. Implement enterprise data governance standards within the Finance domain, defining Finance-specific handling requirements, use case guidance, and operational procedures that extend corporate policies. This includes policies for data classification, retention, privacy, plus AI-specific policies (transparency, bias mitigation, and “hallucination” detection). Draft specific guidelines for the Ethical Use of AI in Finance, ensuring automated forecasting and risk models are transparent and unbiased. Define Clear Ownership: Establish "Data Domains" and assign accountability to Data Stewards within the Finance Org. Establish the scope and priorities for governance initiatives (which domains or systems to focus on) based on business criticality / AI readiness Identify existing data stewards within Finance and formalize their accountability through clear role definitions and decision rights. Align Finance governance practices with enterprise data classification taxonomy and handling standards, adapting enterprise policies for Finance-specific use cases. Be the Data cultural change agent for Finance.
Administration & Execution Operationalize data policies into daily workflows in Finance and upstream at point of entry, ensuring that automated systems and manual entries adhere to established standards. Partner with cross functional data owners to drive upstream data ownership and cleanliness. Define requirements for data transformation logic (dbt, SQL), access control models, define data quality thresholds, and data contracts. Define and govern the Data Lineage Framework for critical financial metrics, establishing the standards for source-to-report traceability (e.g., ensuring 'Revenue' figures are fully auditable to underlying contracts and invoices). Oversee the implementation of these guardrails to ensure end-to-end data integrity across the finance ecosystem. Hold data owners and stewards accountable for data health in their functions. Monitor data quality metrics (Accuracy, Completeness, Timeliness) and lead remediation efforts when data falls below threshold. Communicate risks by framing governance gaps in business impact terms, escalate compliance risks (SOX, CMMC). Define standards and drive adoption through value realization and business impact, not through mandates. Manage the Metadata Repository and Business Data Dictionary to ensure "Revenue" or "Churn" means the same thing in every report. Partner with TDI product owners for technical implementation of governance controls in Finance systems.
Leadership of the Finance Data Governance Council (FDGC) Administer the FDGC program: coordinate meeting logistics, own agendas, facilitate discussions, and execute council decisions. The Council itself comprises VP-level Finance leaders who provide strategic direction and resolve escalated issues. Conflict Resolution: Facilitate agreement on common data definitions, master data, metadata as needed. Roadmap Management: Track and report on the progress of governance initiatives to executive leadership. Translate Risk to Value: Convert complex governance gaps and regulatory risks (SOX, GDPR, EU AI Act) into clear business impact narratives for executive leadership. Demonstrate Business Impact: Align governance efforts to business outcomes to reinforce the value of governance investment.
AI Data Governance & Best Practices Ensure data governance requirements are embedded throughout the model development and deployment lifecycles, including data quality validation, provenance tracking, and bias assessment. Define Finance’s certification standards and governance protocols for 'Gold' data products. Ensure that governed, pre-validated views serve as the authoritative 'Truth' layer to mitigate hallucination and model risk. Regulatory Compliance: Ensure all data practices comply with SOX, GDPR, and emerging AI regulations (like the EU AI Act). Best Practice Evangelism: Conduct training sessions to upskill the Finance team on subjects such as the purpose of governance and the FDGC, data literacy, data quality practices, ethics and AI prompts.