Director, Data Governance (Metadata & Standards)

Career Guide

A Director of Data Governance (Metadata & Standards) leads the company-wide approach for defining, documenting, and standardizing data so it is easy to find, understand, trust, and use safely. This role typically owns the metadata strategy (data definitions, lineage, ownership, quality rules) and sets data standards (naming, formats, reference data) across business and technical teams.

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Key Responsibilities

Define and run the enterprise metadata strategy (business glossary, data catalog, critical data elements, ownership, and stewardship).
Establish data standards (naming conventions, definitions, formats, reference/master data practices) and ensure adoption across teams.
Set governance processes for how data definitions are created, approved, changed, and retired.
Partner with security, privacy, risk, and legal to ensure metadata and standards support compliance requirements (e.g., data classification and retention).
Lead cross-functional governance councils and working groups; align business leaders, data teams, and IT on shared definitions and priorities.
Drive data documentation practices (data dictionaries, reporting definitions, KPI definitions) to reduce confusion and rework.
Oversee data lineage and impact analysis practices to understand how data moves and where it is used.
Define metrics and reporting for governance progress (catalog coverage, glossary adoption, standard compliance, issue resolution time).
Select and manage tooling (data catalog/metadata management, data quality, lineage); ensure tools fit how teams work.
Coach and manage data stewards and governance analysts; build a sustainable operating model.

Top Skills for Success

Stakeholder leadership and influencing without direct authority

Program management (roadmaps, milestones, metrics, operating cadence)

Clear communication and writing (standards, policies, definitions)

Change management and adoption planning

Metadata management (business glossary, data catalog, ownership, lineage concepts)

Data standards design (naming conventions, definitions, reference/master data rules)

Data quality fundamentals (quality dimensions, controls, issue triage workflows)

Data architecture literacy (how data is stored, moved, and transformed in modern platforms)

Data privacy, security, and risk basics (classification, access principles, retention)

Experience with common governance/catalog tools (e.g., Collibra, Alation, Informatica, Microsoft Purview, Atlan)

Career Progression

Can Lead To

VP / Head of Data Governance

Chief Data Officer (CDO) track (in organizations where governance is part of the CDO org)

Director/VP of Data Management or Data Operations

Director/VP of Enterprise Data Management (EDM)

Data Platform or Analytics Leadership roles (when governance is tightly tied to delivery)

Transition Opportunities

Director of Data Quality

Director of Data Privacy & Protection (governance-focused)

Enterprise Data Architect (governance/standards emphasis)

Product Management for Data Platforms or Internal Data Products

Common Skill Gaps

Often Missing Skills

Turning governance into measurable business outcomes (fewer reporting disputes, faster delivery, reduced risk)Practical data lineage and impact analysis implementation (beyond diagrams)Operating model design (clear roles for owners, stewards, and approvers; realistic workflows)Tool adoption strategy (getting teams to actually use the catalog/glossary in daily work)Balancing standards with agility (avoiding bureaucracy while improving consistency)Strong partnership patterns with data engineering and analytics teams

Development SuggestionsBuild a simple, repeatable governance playbook (standards + workflow + metrics) and pilot it with one high-value domain (e.g., customer, product, finance). Focus on adoption: embed metadata capture in delivery processes (definition of done, CI/CD checks where applicable), publish clear examples, and report monthly on progress and impact.

Market Intelligence Report

Director, Data Governance (Metadata & Standards) is part of the Data Governance category.Explore our market intelligence report to see how AI and hiring demand are shifting for these roles.

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Salary & Demand

Median Salary Range

Entry Level$150k–$190k (Director-level hire in smaller orgs/low-cost locations)

Mid Level$190k–$250k

Senior Level$250k–$330k+ (large enterprises; may exclude bonus/equity)

Growth Trend

Strong and growing. Demand is driven by regulatory pressure, cloud data platform adoption, AI/analytics expansion, and the need for consistent definitions across products and reporting.

Companies Hiring

Major Employers

Large banks and insurance companiesHealthcare providers and payersRetailers and e-commerce marketplacesTelecom and media companiesCloud-first software companies with large analytics footprintsGovernment and public sector agenciesManagement consulting and systems integrators (for governance programs)

Industry Sectors

Financial servicesHealthcare and life sciencesRetail and consumer goodsTechnology and SaaSTelecommunicationsManufacturing and supply chainPublic sector

Recommended Next Steps

Assess current state: catalog/glossary coverage, top definition disputes, key regulatory obligations, and the most critical data domains.

Draft a 12-month roadmap with 3–5 measurable goals (e.g., % of priority datasets cataloged, glossary adoption, standard compliance).

Define roles and decision rights: data owners, data stewards, approval process, and escalation path.

Launch a pilot in one domain and deliver quick wins (standard definitions, naming/format standards, and ownership assigned).

Select or optimize a data catalog tool and integrate it into daily workflows (ticketing, data pipelines, and reporting documentation).

Create governance metrics and a simple dashboard for executives (adoption, quality issues, time-to-resolution, coverage).

Prepare interview-ready examples: a standard you rolled out, how you drove adoption, and what business impact it produced.