Senior Manager, Head of Data Governance

Omnissa•USA-FL, FL

6d•Remote

About The Position

Omnissa is seeking a Senior Manager, Head of Data Governance to build and operationalize an enterprise-grade data governance function that underpins our growth, forecasting accuracy, and AI-driven future. This leader will define and enforce how data is owned, defined, governed, and trusted across the company—transforming fragmented data practices into a scalable, auditable, and business-aligned system. This role sits at the intersection of business operations, data architecture, and product telemetry, and is critical to enabling reliable executive reporting and forecasting, scalable GTM and renewals operations, and enterprise AI and automation initiatives.

Requirements

8–12+ years of experience in data governance, data management, or analytics leadership
Proven track record building data governance programs from the ground up
Strong understanding of data modeling and data architecture
Strong understanding of data quality frameworks
Strong understanding of metadata, lineage, and cataloging concepts
Experience working with CRM systems (e.g., Salesforce)
Experience working with data warehouses (e.g., Snowflake or similar)
Experience working with BI tools (e.g., Tableau, Power BI)
Experience with modern data catalog / governance tools (e.g., Atlan, Informatica, Collibra)

Nice To Haves

Experience in SaaS or subscription-based business models
Familiarity with CPQ systems, renewals forecasting, and revenue operations
Exposure to AI/ML or data-driven automation initiatives
Experience in a federated data environment

Responsibilities

Build the Enterprise Data Governance Function: Establish Omnissa’s data governance operating model, including data ownership (business + technical), data stewardship framework, and governance forums and decision rights. Develop and maintain a business data glossary (ARR, UFR, expansion, etc.). Define and enforce enterprise data standards and policies.
Own Data Quality & Data Integrity at Scale: Define data quality KPIs and SLAs for business-critical data. Implement processes for monitoring data health and managing data issues and remediation workflows. Partner with data engineering to embed data quality controls into pipelines.
Establish a Trusted Data Foundation for Executive Decision-Making: Ensure all executive reporting is based on certified, governed datasets. Eliminate conflicting metric definitions across Finance, GTM, and Product. Partner with stakeholders to create single sources of truth for key metrics.
Enable AI & Data-Driven Automation: Define requirements for AI-ready data architecture, including metadata, lineage, and semantic consistency. Support development of agentic workflows (e.g., account hierarchy logic, deal classification). Ensure governance frameworks support explainability and auditability.
Implement and Operationalize Data Catalog & Governance Tooling: Lead deployment and adoption of tools such as Atlan (or equivalent). Ensure accurate data lineage, asset certification workflows, and broad adoption across business teams. Embed governance into daily workflows—not as a separate system.
Lead Cross-Functional Alignment: Partner closely with Finance, GTM Operations (Sales, Renewals), and Product & Engineering. Resolve conflicts around metric definitions, data ownership, and reporting logic.