Director, Enterprise Data Governance & Metadata

Career Guide
A Director of Enterprise Data Governance & Metadata sets the rules, standards, and accountability for how an organization creates, defines, secures, shares, and uses data. This role typically leads enterprise-wide programs that improve data trust (quality, consistency, compliance) and make data easier to find and use through clear definitions, catalogs, and ownership.

Key Responsibilities

  • Create and run the enterprise data governance program (policies, decision-making forums, roles, and ways of working).
  • Define data ownership and accountability (e.g., assigning business owners and technical stewards for critical data).
  • Lead metadata strategy so people can understand and locate data (data catalog adoption, business glossary, definitions, and lineage where possible).
  • Set standards for data quality and oversee how issues are identified, prioritized, fixed, and prevented.
  • Partner with security, privacy, legal, and risk teams to ensure proper data handling and regulatory compliance (e.g., retention, access rules, consent where applicable).
  • Establish key enterprise data standards (naming conventions, reference data, master data alignment, documentation expectations).
  • Drive adoption with business leaders by demonstrating value (faster reporting, fewer reconciliations, reduced risk, better AI/analytics outcomes).
  • Manage governance tooling selection and rollout (data catalog, quality monitoring, policy workflow tools) and ensure they are used effectively.
  • Build and lead a team (data governance managers, metadata specialists, data quality leads) and coordinate a network of cross-functional data stewards.
  • Define and track metrics (catalog usage, data quality scorecards, issue resolution time, policy compliance, audit readiness).
  • Oversee change management: training, communications, and stakeholder engagement across business and technology teams.
  • Align governance priorities to business strategy and major programs (cloud migration, ERP/CRM rollouts, AI initiatives, M&A integration).

Top Skills for Success

Stakeholder leadership and influence across business and technology teams
Program leadership (setting priorities, roadmaps, governance forums, and measurable outcomes)
Clear communication and training (turning policies into practical guidance people will follow)
Policy and standards design (data ownership, access rules, retention, classification)
Metadata management (business glossary, data catalog, and improving “findability” and understanding of data)
Data quality management (scorecards, issue workflows, root-cause prevention)
Privacy, risk, and compliance understanding (working knowledge of relevant regulations and audit expectations)
Data architecture literacy (how data flows through systems; ability to partner with engineers/architects)
Tooling knowledge (data catalogs, governance workflow tools, data quality tools) and vendor evaluation
Operating model design (roles, responsibilities, escalation paths, decision rights)

Career Progression

Can Lead To
VP / Head of Data Governance
VP Data Management / Data Strategy
Chief Data Officer (in some organizations)
Head of Data Risk & Compliance
Head of Data Product (where governance is embedded into data products)
Transition Opportunities
Enterprise Data Architect (strategy-focused)
Director of Data Management (master/reference data, data operations)
Director of Data Quality
Privacy or Data Risk leadership roles (especially in regulated industries)

Common Skill Gaps

Often Missing Skills
Demonstrating measurable business value (connecting governance work to revenue, cost, risk reduction, or speed-to-delivery)Change management at scale (driving adoption beyond a single team or project)Practical metadata outcomes (getting teams to maintain definitions and documentation consistently)Data quality operations (repeatable workflows, prioritization, and prevention rather than one-off fixes)Privacy and regulatory depth for the organization’s specific industryTooling implementation experience (leading selection, rollout, and sustained usage of catalogs/governance platforms)Working effectively with modern data platforms (cloud warehouses/lakes) and data product teams
Development SuggestionsBuild a portfolio of 2–3 concrete governance wins (e.g., a critical data domain with named owners, published definitions, a quality scorecard, and a working issue process). Pair that with a clear operating model and adoption metrics to show impact, not just policy creation.

Salary & Demand

Median Salary Range
Entry LevelTypically not an entry-level role; comparable “Manager, Data Governance” roles often range $130k–$170k (US, base).
Mid Level$170k–$230k (US, base) for Director-level roles; total compensation often higher with bonus/equity depending on industry.
Senior Level$230k–$320k+ (US, base) for Senior Director/VP-track roles; total compensation can be significantly higher in large enterprises and tech.
Growth Trend
Strong and increasing demand, driven by regulatory pressure, cloud data expansion, data sharing across teams, and the need for trusted data for analytics and AI.

Companies Hiring

Major Employers
JPMorgan ChaseBank of AmericaWells FargoGoldman SachsCitigroupUnitedHealth GroupCVS HealthCignaPfizerJohnson & JohnsonWalmartTargetAmazonMicrosoftGoogleIBMAccentureDeloitteKPMGPwC
Industry Sectors
Financial services (banking, insurance, capital markets)Healthcare and life sciencesRetail and e-commerceTechnology and software platformsTelecommunicationsManufacturing and supply chainEnergy and utilitiesPublic sector and government contractorsConsulting and professional services

Recommended Next Steps

1
Define your “governance operating model” one-pager: decision forums, roles (owner/steward/custodian), escalation path, and success metrics.
2
Create a 90-day plan template you can reuse in interviews: pick 1–2 high-value data domains, stand up ownership, publish definitions, and launch a quality scorecard.
3
Strengthen metadata credibility: lead or simulate a data catalog rollout plan (onboarding approach, minimum required fields, ongoing stewardship cadence).
4
Quantify outcomes: track baseline vs. improved metrics (reporting time, reconciliation effort, data incident counts, audit findings, duplicate customer records).
5
Fill compliance gaps: map how privacy/security requirements translate into data handling rules (classification, access reviews, retention).
6
Build a leadership narrative: prepare examples of influencing executives, resolving conflicts over data definitions, and getting teams to adopt standards.
7
If job searching: tailor your resume to show enterprise scale (number of systems/domains, governed data elements, catalog adoption rates, quality improvements) and cross-functional leadership.