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The Data Governance Lead will build and run the governance capability of AHEAD ’s Data Platform Team — hands-on. This is not a standards-writing or policy-committee role. The Lead will define governance standards and then implement them directly: building the catalog, wiring lineage, writing quality checks into pipelines, configuring access policy at the platform level, and embedding governance controls into every data product before it is published.
The role sits within the Data Platform Team, reporting to the Director, Data Platform. The Lead is a direct execution partner to the Director, responsible for making governance tangible and operational across every domain connection, data product, and pipeline the team owns. Governance here is an engineering discipline as much as a policy one — the Lead is expected to write, configure, and ship alongside the engineers they work with.
The Data Governance Lead will help the team deliver on its federated product model, its domain connection contract standards, its lineage and classification requirements, and its AI-safe data use controls. This leader will also build and lead a small governance function over time as the platform matures.
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.