Data Product Governance Lead

Duties and Responsibilities

Data Product Standards and Publication 
Define the standards a data product must meet before it is published for broader consumption, and update those standards as the platform and its consumers evolve.
Establish the required information for published data products, including business and technical definitions, ownership, authoritative source, refresh expectations, lineage, quality measures, classification, access requirements, and applicable data contracts.
Define publication and certification standards that align with AHEAD's medallion architecture, including the requirements data must meet as it progresses from raw and standardized layers to trusted, reusable data products.
Define and maintain publication criteria for data products contributed by domain teams and work with Data Platform engineers to automate those checks where practical.
Define certification and readiness indicators that show consumers whether a data product is supported, current, and appropriate for use, including how changes in quality, freshness, or ownership affect that status.
Create reusable templates, patterns, and guidance that help domain teams meet publication requirements consistently. 
Metadata, Lineage, Quality, and Contract Standards 
Establish metadata standards that give consumers useful business and technical context without creating unnecessary documentation overhead.
Define lineage requirements across source systems, ingestion, transformation layers, published data products, and downstream consumption, and identify gaps where automated capture needs to improve.
Define data quality requirements for published data products, including measures for freshness, completeness, validity, consistency, and reliability, and establish a common way to represent quality and health across the platform.
Partner with domain teams and engineers to implement dataset-specific quality rules, thresholds, and monitoring, and make quality status visible to consumers through the catalog.
Define standards for data contracts, including schema, semantics, ownership, service expectations, versioning, and change management across batch, API, and event-driven data products.
Establish clear expectations for changes to published data products so downstream consumers can prepare for them. 
Classification, Access, and Governed Data Use 
Translate enterprise classification and access standards into data product requirements and platform controls.
Work with Snowflake capabilities, including RBAC, tagging, classification, masking, and row- and column-level controls, to protect PII and other restricted information.
Develop repeatable access patterns that support self-service consumption while maintaining appropriate controls.
Implement data product governance requirements that support approved AI and agent-based consumption, including sensitive data handling, access, and traceability.
Work with Security, Risk, Legal, and Architecture teams to align data product governance with privacy, security, compliance, and enterprise requirements. 
Product Ownership
Catalog, Lineage, and Governance Capabilities Own the backlog and roadmap for the data catalog, metadata, lineage, publication, certification, and related governance capabilities.
Work hands-on with Snowflake Horizon Catalog and other platform capabilities used for discovery, metadata, lineage, classification, and trust.
Treat data publishers and consumers as customers of the Data Platform, regularly gathering feedback to understand friction, unmet needs, and opportunities to improve the publishing and consumption experience.
Translate publisher and consumer feedback into prioritized platform requirements and enhancements for the engineering team.
Define and monitor measures such as metadata completeness, lineage coverage, quality coverage, certified product adoption, catalog usage, and time to publish, and use feedback and adoption data to improve the platform and standards. 
Partnership and Enablement 
Work directly with Data Platform engineers throughout design and delivery, reviewing designs for governance fit and writing clear acceptance criteria.
Partner with source-system and domain teams to identify authoritative sources and establish scalable publication patterns that support the federated model.
Help establish clear ownership and accountability for published data products across business and technology teams.
Collaborate with Analytics, Software Engineering, Architecture, Security, and business teams on data product requirements and platform improvements.
Contribute to AHEAD's broader data governance strategy and priorities as the platform evolves. 

Education and Experience

Bachelor's degree or equivalent practical experience. 
7+ years of experience across data governance, data products, data platforms, data architecture, analytics engineering, technical product management, or related disciplines.
Hands-on experience implementing governance capabilities, including catalog, metadata, and lineage, in a modern cloud data platform.
Experience defining and implementing data product standards, including ownership, certification criteria, documentation requirements, discoverability, and lifecycle management.
Experience defining data quality and data contract standards, including measures such as freshness and completeness, schema versioning, and change management.
Working knowledge of platform governance controls such as RBAC, data classification, masking, and row- and column-level access.
Experience owning a technical product backlog or roadmap and prioritizing work based on user and business needs.
Experience working directly with data engineers, including translating policy and user needs into engineering-ready requirements.
Strong communication skills and the ability to influence business and technology teams that do not report to you. 

Preferred Experience 

Experience with Snowflake or Snowflake Horizon Catalog, or comparable catalog and lineage platforms.
Experience with medallion architecture and data product publication patterns in a modern data platform.
Experience automating metadata, quality, contract, or publication requirements through pipelines, APIs, CI/CD, or platform tooling.
Experience with federated or domain-oriented data product models.
Experience in regulated, security-sensitive, or compliance-driven environments, or with PII and other sensitive enterprise data.
Experience governing schemas and contracts for streaming or messaging environments such as Kafka or MuleSoft.
Experience developing self-service data discovery, publishing, or access capabilities.
Familiarity with governing data for AI and agent-based use cases. 

Physical Requirements

• Ability to safely and successfully perform the essential job functions consistent with the ADA, FMLA and other federal, state and local standards, including meeting qualitative and/or quantitative productivity standards.
• Ability to maintain regular, punctual attendance consistent with the ADA, FMLA and other federal, state, and local standards.
• Primarily office and computer-based work with standard technical leadership and collaboration expectations for a platform engineering role.