Director, Data Governance & Metadata Strategy

Career Guide
A Director of Data Governance & Metadata Strategy sets the rules, standards, and operating model for how an organization defines, manages, and trusts its data. This role connects business leaders, data teams, and risk/compliance partners to ensure data is understandable (clear definitions), findable (good catalogs/labels), protected (appropriate access and privacy), and usable for analytics and AI. The job often blends program leadership, stakeholder management, and the practical rollout of tools and processes that make data easier to discover and safer to use.

Key Responsibilities

  • Define and lead the enterprise data governance program (policies, decision rights, and ways of working).
  • Create and maintain a business-friendly metadata strategy (consistent definitions, ownership, and documentation for key data).
  • Establish data ownership/stewardship roles and ensure responsibilities are clear and measurable.
  • Prioritize and drive improvements to data quality for critical data (e.g., customer, product, financial reporting).
  • Oversee or partner on a data catalog and business glossary rollout so teams can find and understand data quickly.
  • Set standards for data classification and handling (e.g., sensitive vs. non-sensitive) and align with privacy/security requirements.
  • Align governance practices with analytics, reporting, and AI initiatives so new use cases don’t create new risk.
  • Design governance processes that are lightweight and adoptable (intake, approvals, issue management, exception handling).
  • Define metrics and reporting (adoption, data quality indicators, policy compliance, time-to-find-data).
  • Lead cross-functional committees and working groups; manage stakeholders and resolve conflicts.
  • Partner with technology teams on data architecture practices that support governance (lineage, standard naming, retention).
  • Manage budget, vendors, and a team of data governance/metadata professionals; coach and develop talent.

Top Skills for Success

Program leadership (setting a roadmap, milestones, and measurable outcomes)
Executive stakeholder management and influencing without direct authority
Clear communication and change management (making new processes easy to adopt)
Data governance operating models (roles, decision rights, policy frameworks)
Metadata management (business glossary, data catalog adoption, definitions, ownership)
Data quality management (profiling, rules, monitoring, issue resolution workflow)
Privacy, risk, and compliance alignment (e.g., retention, access controls, audit readiness)
Understanding modern data platforms (cloud data warehouses/lakes, integration, analytics tooling)
Data lineage concepts (knowing where data comes from and how it changes)
Vendor/tool evaluation and implementation (selecting and rolling out governance/catalog tools)

Career Progression

Can Lead To
VP / Head of Data Governance
Chief Data Officer (CDO)
Head of Data Management or Data Office
Director/VP of Data & Analytics Operations
Director of Data Risk / Data Compliance (in regulated sectors)
Transition Opportunities
Data Product Leadership (if focusing on “data as a product” and domain ownership)
Enterprise Architecture or Data Architecture leadership (if leaning technical)
Information Security or Privacy program leadership (if leaning risk/compliance)
AI Governance leadership (model risk, policy, and responsible AI oversight)

Common Skill Gaps

Often Missing Skills
Turning governance into measurable business value (beyond policies and meetings)Practical metadata rollout (getting teams to actually use catalogs/glossaries)Data quality operating rhythm (monitoring, triage, root-cause fixes with owners)Balancing control with speed (avoiding overly complex approval processes)Hands-on familiarity with modern data tools and how teams build pipelinesClear accountability models (who owns which data, and what “good” looks like)
Development SuggestionsBuild a portfolio of 2–3 concrete wins (e.g., improved reporting accuracy, reduced time to find data, faster audit responses). Pair policy work with adoption tactics: training, embedded stewards, simple templates, and metrics. Spend time with engineering/analytics teams to understand their workflows and remove friction rather than adding steps.

Salary & Demand

Median Salary Range
Entry LevelUS$170k–$220k (smaller scope director, limited enterprise coverage)
Mid LevelUS$220k–$290k (enterprise scope, multiple domains, strong stakeholder leadership)
Senior LevelUS$290k–$400k+ (large global programs; may include bonus/equity; varies heavily by industry and company size)
Growth Trend
Strong and steady demand, driven by regulatory pressure, cloud modernization, and the push to make data/AI initiatives more reliable. Hiring is especially active in regulated industries and larger organizations consolidating data platforms.

Companies Hiring

Major Employers
JPMorgan ChaseBank of AmericaWells FargoCitiCapital OneUnitedHealth Group / OptumCVS Health / AetnaCignaKaiser PermanentePfizerMerckJohnson & JohnsonComcastVerizonAT&TWalmartTargetAmazonMicrosoftGoogleSalesforce
Industry Sectors
Financial services (banking, payments, insurance)Healthcare providers and health insurersPharma and life sciencesTelecommunications and mediaRetail and e-commerceTechnology platforms and SaaSEnergy and utilitiesGovernment and higher education (data standardization and reporting)

Recommended Next Steps

1
Define your target scope: enterprise-wide governance vs. a few priority domains (customer, finance, product, etc.).
2
Create a one-page governance operating model (roles, decision rights, meeting cadence, escalation path).
3
Assess current metadata maturity: do teams have consistent definitions, owners, and a searchable catalog?
4
Pick 1–2 high-impact use cases (e.g., regulatory reporting, customer 360, revenue reporting) to anchor the program.
5
Stand up a basic data quality process: rules for critical fields, monitoring, and a clear issue ownership workflow.
6
Align with security/privacy early; ensure data classification and access rules are practical and enforceable.
7
Select or optimize a catalog/glossary tool and measure adoption (active users, documented datasets, search-to-use rate).
8
Build executive-ready metrics (time-to-find-data, number of critical elements with owners, quality trend, audit findings).
9
Develop your leadership narrative for interviews: how you reduced risk and increased data usability at the same time.