Director, Data Governance (Metadata & Reference Data)

Career Guide

A Director of Data Governance (Metadata & Reference Data) sets the rules, standards, and operating model that keep an organization’s data easy to find, correctly defined, consistently used, and trusted across teams. This role leads the strategy and execution for metadata (the “labels and descriptions” about data) and reference data (shared lists and codes like country codes, product categories, customer types) so reporting, analytics, operations, and regulatory needs all rely on the same definitions.

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Key Responsibilities

Define and lead the enterprise approach for data governance focused on metadata and reference data
Set data standards: common definitions, naming rules, ownership, and approval workflows
Establish and run governance forums (working groups and decision bodies) to resolve definition conflicts and prioritize work
Oversee the data catalog program so employees can discover data, understand meaning, and see quality and usage guidance
Create and maintain a reference data strategy: what lists/codes exist, where they are mastered, and how changes are controlled
Partner with business leaders to agree on critical data definitions (e.g., “active customer,” “net revenue,” “product family”)
Implement processes for data owners and stewards to manage metadata and reference data on an ongoing basis
Define and track key measures (adoption, completeness of metadata, reference data consistency, issue resolution time)
Coordinate with security, privacy, risk, and compliance teams to ensure governance supports policy needs
Guide tool selection and configuration (catalog, lineage tracking, reference data management) and ensure tools fit real workflows
Manage a team (governance leads, data stewards, analysts) and a roadmap of initiatives across departments
Drive change management: training, communications, and support so teams actually use the standards and tools

Top Skills for Success

Stakeholder leadership and influencing (aligning many teams on one set of definitions)

Program and roadmap management (prioritizing work, milestones, and measurable outcomes)

Clear communication and change management (training, adoption, handling pushback)

Metadata management (data catalog content, definitions, ownership, and usage guidance)

Reference data management (shared codes/lists, mastering, and controlled change processes)

Data quality fundamentals (how issues are found, prioritized, fixed, and prevented)

Data architecture basics (how data flows from sources to reports; major systems involved)

Risk, privacy, and compliance awareness (how governance supports controls and audits)

Operating model design (roles like owner/steward, decision rights, and workflows)

Tooling knowledge (data catalog and reference data tools; integration with data platforms)

Career Progression

Can Lead To

VP / Head of Data Governance

Head of Data Management

Chief Data Officer (CDO) track roles

Director/VP of Data Strategy

Data Platform or Data Product leadership (in organizations that run data as products)

Transition Opportunities

Director of Data Quality

Director of Master Data Management (MDM)

Director of Data Risk & Controls

Enterprise Data Architect (leadership track)

Analytics/BI Governance leader

Common Skill Gaps

Often Missing Skills

Turning governance into measurable business outcomes (fewer reporting disputes, faster delivery, reduced rework)Practical experience implementing and driving adoption of a data catalog (not just selecting a tool)Reference data change control at scale (versioning, approvals, and distribution to downstream systems)End-to-end understanding of how definitions affect metrics and regulatory reportingClear decision-making frameworks (who decides, how conflicts are resolved, and timelines)Strong partnership model with engineering and analytics teams (governance that fits delivery workflows)

Development SuggestionsBuild a portfolio of 2–3 real governance wins (e.g., standardizing key metrics, improving catalog adoption, reducing reference data inconsistencies). Document baseline vs. after results, decision process, and adoption approach. Strengthen technical fluency enough to work smoothly with data engineering teams, while keeping focus on business alignment and measurable outcomes.

Market Intelligence Report

Director, Data Governance (Metadata & Reference Data) 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 in smaller markets/companies; limited scope)

Mid Level$190k–$240k (typical enterprise Director scope; may include bonus)

Senior Level$240k–$320k+ (large enterprise/global scope; may include significant bonus/equity)

Growth Trend

Strong and steady. Demand is driven by regulatory expectations, AI and analytics initiatives needing trusted definitions, cloud data platform growth, and business pressure to reduce reporting inconsistencies. Hiring is especially active in regulated industries and large enterprises modernizing data platforms.

Companies Hiring

Major Employers

JPMorgan ChaseBank of AmericaWells FargoGoldman SachsMorgan StanleyUnitedHealth GroupCVS HealthElevance HealthCignaPfizerNovartisMerckComcastVerizonAT&TWalmartTargetAmazonMicrosoftGoogleIBM

Industry Sectors

Banking and capital marketsInsuranceHealthcare providers and payersPharmaceuticals and life sciencesTelecommunicationsRetail and e-commerceTechnology and cloud servicesManufacturing and supply chainEnergy and utilitiesGovernment and public sector

Recommended Next Steps

Create a one-page governance operating model: roles (owner/steward), decision forums, and escalation path

Pick 3–5 high-value business terms and produce “gold standard” definitions with owners, examples, and where-used reporting

Design a reference data lifecycle: request → review → approval → publish → monitor, including timing and accountability

Set practical success metrics (catalog adoption, metadata completeness for priority datasets, reference data change turnaround time)

Audit current metadata coverage: which key datasets lack owners, definitions, or usage guidance; prioritize fixes

Strengthen your executive story: how metadata and reference data reduce risk, speed delivery, and improve trust in numbers

If job searching: tailor your resume to outcomes (conflicts resolved, standards adopted, time saved) rather than tool lists

Network with adjacent leaders (Data Engineering, BI/Analytics, Risk/Compliance) and ask for the top 3 “data pain points” to target