Data Product Governance Lead

United StatesRemoteFull-timelead

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Quick Summary

Overview

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Product Governance Lead based in United States.

Technical Tools

OtherLead

This is a strategic and hands-on data governance leadership role focused on making data products discoverable, trusted, well-documented, and ready for broad consumption. You will define the standards, controls, and capabilities that enable data publishers and consumers to work effectively within a modern cloud data platform. The role combines governance strategy with technical product ownership, working closely with Data Platform engineers to turn requirements into scalable platform capabilities and automated controls. You will shape catalog, lineage, metadata, certification, data quality, and data contract practices while supporting secure and governed data use. As the platform matures, you will also have the opportunity to build and lead a dedicated governance function. This remote position offers significant influence over how data products are published, managed, discovered, and consumed across the organization.

Define and continuously improve the standards that data products must meet before being published for broader consumption, including ownership, business and technical definitions, authoritative sources, refresh expectations, lineage, quality measures, classification, access requirements, and data contracts.
Establish publication and certification requirements aligned with a medallion architecture, ensuring data progresses from raw and standardized layers into trusted, reusable data products with clear readiness indicators.
Partner with Data Platform engineers to automate publication checks, certification criteria, quality controls, and other governance requirements wherever practical.
Develop reusable templates, patterns, and guidance that help domain teams consistently meet data publication and documentation standards without creating unnecessary administrative overhead.
Establish standards for metadata, lineage, data quality, and data contracts, including freshness, completeness, validity, consistency, schema versioning, ownership, service expectations, and change management.
Partner with domain teams and engineers to implement dataset-specific quality rules and monitoring, while ensuring data quality and health indicators are visible to consumers through the data catalog.
Translate enterprise classification, privacy, security, and access standards into practical data product requirements and platform controls, including RBAC, tagging, masking, and row- and column-level access controls.
Establish repeatable access patterns that enable self-service data consumption while protecting PII and other sensitive information, including requirements for approved AI and agent-based data use.
Own the backlog and roadmap for catalog, metadata, lineage, publication, certification, and related data governance capabilities, prioritizing improvements based on user and business needs.
Work hands-on with Snowflake Horizon Catalog and other platform capabilities supporting discovery, metadata, lineage, classification, and data trust.
Treat data publishers and consumers as platform customers by gathering feedback, identifying friction and unmet needs, and translating those insights into prioritized engineering requirements and enhancements.
Define and monitor governance and adoption metrics such as metadata completeness, lineage coverage, quality coverage, certified product adoption, catalog usage, and time to publish.
Collaborate with Data Platform engineers, domain teams, Analytics, Software Engineering, Architecture, Security, and business stakeholders to establish ownership, improve publishing patterns, and advance the broader data governance strategy.
As the platform matures, build and lead a small governance function that supports consistent adoption of data product standards and practices.

Requirements

~2 min read

Hold a Bachelor's degree or equivalent practical experience in a relevant discipline.
Bring 7+ years of experience across data governance, data products, data platforms, data architecture, analytics engineering, technical product management, or related fields.
Have hands-on experience implementing governance capabilities such as data catalogs, metadata management, and lineage within a modern cloud data platform.
Demonstrate experience defining and implementing data product standards covering ownership, certification, documentation, discoverability, and lifecycle management.
Have experience establishing data quality and data contract standards, including freshness, completeness, schema versioning, service expectations, and change management.
Possess working knowledge of platform governance controls such as RBAC, data classification, masking, and row- and column-level access.
Have experience owning a technical product backlog or roadmap and prioritizing platform capabilities according to user and business requirements.
Be experienced in working directly with data engineers and translating policies, governance requirements, and user needs into clear, engineering-ready specifications and acceptance criteria.
Demonstrate strong communication and stakeholder-management skills, with the ability to influence business and technology teams without direct reporting authority.
Experience with Snowflake, Snowflake Horizon Catalog, or comparable catalog and lineage platforms is preferred.
Experience with medallion architecture, federated or domain-oriented data product models, and modern data publication patterns is highly desirable.
Experience automating metadata, quality, contract, or publication requirements through pipelines, APIs, CI/CD, or platform tooling is a plus.
Experience working in regulated, security-sensitive, or compliance-driven environments, particularly with PII and other sensitive enterprise data, is desirable.
Familiarity with governing schemas and contracts for streaming or messaging environments such as Kafka or MuleSoft, as well as self-service data discovery, publishing, or access capabilities, is beneficial.
Familiarity with data governance for AI and agent-based use cases is an additional advantage.
Be able to perform the essential functions of the role in a primarily computer-based environment and maintain regular, punctual attendance consistent with applicable workplace standards.