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, 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 data 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 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 mainta