Data Governance Lead

Duties and Responsibilities

Platform governance execution — define and build

• Build and operate the enterprise data catalog: onboard domains and data products, define and enforce metadata standards, and ensure every published product in the platform catalog has complete owner, SLA, classification, lineage, and contract documentation.
• Implement automated lineage capture across ingestion pipelines, medallion transformations, and data product publication
• Write and maintain data quality rules at the pipeline level: define the checks, implement them in Bronze/Silver/Gold processing layers, configure alerting, and own remediation workflows when quality thresholds are breached.
• Configure and enforce Snowflake-level governance controls: role design, row- and column-level security, data classification tags, masking policies, and access policy enforcement — working directly in the platform.
• Build the access request and approval workflow for domain connections — the self-service path that lets consumers discover what they can access, request what they cannot, and receive the right scoped access without manual escalation.
• Implement event contract governance: define schema standards, configure schema registries, set retention and access policies on domain event streams, and ensure event contracts are documented and enforced at the connection layer.
• Build and maintain governance dashboards and metrics: stewardship coverage, quality pass rates, metadata completeness, lineage coverage, policy adoption, and access request SLA — instrumented, not reported manually.

Standards, operating model, and policy

• Define and own the enterprise data governance strategy, standards, and roadmap in alignment with the Data Platform strategy and business priorities — then execute against it personally and through the team.
• Establish the governance operating model: stewardship roles and expectations across business and technology teams, decision rights, escalation paths, policy lifecycle, and governance forum cadence.
• Own the enterprise agenda across data ownership, stewardship, quality, metadata, lineage, cataloging, classification, retention, and policy adoption.
• Define governance standards for how data is created, documented, classified, accessed, shared, retained, and monitored — then implement those standards into platform tooling and processes
• Define and implement the federated product review gate: the governance standards a domain-team-contributed data product must meet before the core team promotes it into the shared catalog. Own the review process and execute it.
• Design and implement policy-aware controls for AI and Agent Operations: data usage guardrails, sensitive-data handling, access requirements, traceability of context, auditability of actions, and safe use of governed write-back patterns.
• Work with Security, Risk, Legal, and Architecture leaders to align governance policies and controls with privacy, security, compliance, and enterprise standards.
• Drive governance measures and KPIs: stewardship participation, policy adoption rates, data quality performance, metadata coverage, lineage completeness, and issue resolution time.

Cross-functional partnership and enablement

• Work directly with platform engineers on pipeline and product delivery: review designs for governance fit before build, and implement governance controls alongside engineers during delivery sprints.
• Support domain teams participating in the federated contribution model: provide standards guidance, review submitted products, and give structured feedback that helps contributors meet the governance bar rather than just rejecting submissions.
• Define governance requirements for shared business definitions, reference data patterns, master data, and entity resolution needed to support reporting, automation, semantic consumption, and AI workflows.
• Build governance workflows that reduce friction for engineering, product, analytics, and business teams while maintaining the quality and control standards the platform requires.

Team leadership

• Build and lead a small governance function over time as the platform matures, hiring and developing governance engineers and stewards who can execute alongside the platform team.
• Provide strong cross-functional leadership, influencing both business and technology stakeholders to build durable data accountability and disciplined data practices across the enterprise.

Education and Experience

Education and Experience

• Bachelor’s degree or equivalent experience.
• 8 or more years of experience across data governance, data platform engineering, data architecture, analytics engineering, or related roles — with a strong bias toward hands-on platform delivery alongside governance.
• At least 3 years in a leadership or lead role with responsibility for governance programs, stewardship models, or cross-functional data operating frameworks.
• Demonstrated experience building and operating governance capabilities in a modern cloud data platform environment — not just defining policy, but implementing it in tooling.
• Hands-on experience configuring Snowflake governance controls: RBAC design, row/column-level security, classification tags, masking policies, and access governance at the platform level.
• Hands-on experience implementing data catalogs and lineage tools — onboarding assets, configuring automated lineage capture, defining metadata standards, and operating the catalog as a live platform service.
• Experience writing and maintaining data quality rules within data pipelines: defining quality dimensions, implementing checks in transformation layers, and owning remediation workflows.
• Experience governing event contracts and schemas in a streaming or messaging environment (Kafka, MuleSoft, or equivalent): schema standards, registry configuration, retention policy, access rights.
• Experience defining and executing data product governance standards: ownership, certification criteria, documentation requirements, discoverability, and lifecycle management.
• Experience designing and implementing governance controls for AI or agent-based use cases: data usage guardrails, sensitive-data access controls, auditability of actions, and traceability of context.
• Experience working embedded within an engineering team, participating in delivery sprints, reviewing designs, and implementing governance controls alongside engineers.
• Strong understanding of data governance disciplines: ownership, stewardship, quality, metadata, lineage, cataloging, classification, retention, and policy adoption.
• Experience in regulated, security-sensitive, or compliance-driven environments is strongly preferred.
• Strong communication skills with the ability to translate governance requirements into practical engineering patterns and business expectations.
• Strong systems thinking: understanding how governance must thread through platform architecture, engineering delivery, and business consumption — not sit above them.
• Practical understanding of how governance must evolve to support AI, automation, and agent-based execution safely.

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.