Director, Data Governance (Metadata & Definitions)

Career Guide
A Director of Data Governance (Metadata & Definitions) leads the strategy and execution for how an organization describes, defines, and labels its data so people can find it, trust it, and use it consistently. This role typically owns the business glossary (common definitions), metadata standards (information about data), and the processes and partnerships needed to keep definitions accurate across systems, teams, and reports.

Key Responsibilities

  • Set the company-wide strategy for data definitions, naming standards, and ownership (who is responsible for what data).
  • Build and run a business glossary and metadata catalog so employees can discover data and understand what it means.
  • Create governance processes (review, approval, change control) to keep definitions consistent over time.
  • Partner with business leaders to align on key metrics and definitions (e.g., revenue, active customer, churn).
  • Work with data, analytics, and engineering teams to connect definitions to dashboards, reports, and data pipelines.
  • Establish roles and routines for data owners and data stewards (people responsible for maintaining definitions and quality).
  • Track and report governance adoption and impact (usage, coverage of key domains, reduction in conflicting metrics).
  • Support risk and compliance needs by improving traceability (where data comes from, how it is used) and audit readiness.
  • Select, implement, or improve tooling for metadata management and documentation, and drive user adoption.
  • Lead, coach, and grow a governance team; manage budgets, priorities, and stakeholder expectations.

Top Skills for Success

Stakeholder leadership and influence (aligning executives and teams on shared definitions)
Clear communication and documentation (writing definitions that business and technical teams both understand)
Program management (prioritization, timelines, operating rhythm, measurable outcomes)
Data literacy and analytics fundamentals (how data is modeled, reported, and used for decisions)
Metadata management and business glossary design (standards, ownership, lifecycle of definitions)
Data quality and controls (rules, monitoring, issue management, root-cause workflows)
Data lineage and traceability (understanding where data comes from and how it changes)
Tooling knowledge (data catalog/glossary platforms, documentation workflows, integration with BI)
Operating in regulated environments (privacy, retention, access controls, audit needs)
Change management and adoption (driving consistent usage across departments)

Career Progression

Can Lead To
VP / Head of Data Governance
Head of Data Management
Chief Data Officer (CDO) track
Director / VP of Data Strategy
Director of Data Products (with strong business-alignment experience)
Transition Opportunities
Director of Analytics Enablement / BI (if focused on metric definitions and reporting consistency)
Director of Data Quality (if focused on controls and monitoring)
Director of Data Risk & Compliance (if focused on audit, privacy, and controls)

Common Skill Gaps

Often Missing Skills
Turning governance into measurable outcomes (adoption metrics, time saved, fewer conflicting KPIs)Strong business glossary design (definition templates, ownership, approval and change process)Connecting definitions to real usage points (dashboards, reports, self-serve analytics)Practical lineage and traceability (beyond diagrams—usable for impact analysis and audits)Tool integration planning (how catalogs/glossaries connect to data platforms and BI tools)Change management at scale (training, communications, incentives, and making it part of daily work)
Development SuggestionsBuild a portfolio of 2–3 governance outcomes (e.g., standardized customer definitions, reduced reporting disputes, faster onboarding of analysts). Practice writing concise definitions with examples and edge cases. Learn how metadata tools are integrated with BI and data platforms in your organization. Establish a repeatable operating model: owners/stewards, review cadence, and a simple intake-and-approval workflow.

Salary & Demand

Median Salary Range
Entry LevelUS$140k–$185k (often requires prior governance/analytics leadership; true entry into director level is uncommon)
Mid LevelUS$180k–$240k
Senior LevelUS$230k–$320k+ (higher with large scope, high-regulation industries, or major transformation programs)
Growth Trend
Growing demand. Organizations are investing more in trusted, consistent data for analytics and AI, and in stronger controls for privacy, security, and regulatory expectations. Hiring tends to increase during data platform modernizations, cloud migrations, and AI/BI scaling initiatives.

Companies Hiring

Major Employers
JPMorgan ChaseBank of AmericaWells FargoCapital OneCitiUnitedHealth Group / OptumCVS Health / AetnaKaiser PermanenteComcastVerizonAmazonMicrosoftGoogleSalesforceWalmart
Industry Sectors
Financial services (banking, insurance, payments)Healthcare providers and insurersPharmaceuticals and life sciencesTelecommunicationsRetail and e-commerceTechnology and softwareEnergy and utilitiesGovernment and public sector

Recommended Next Steps

1
Create a 90-day plan template: top data domains, key metrics to standardize, and adoption targets.
2
Draft a business glossary standard: required fields (definition, owner, source, calculation notes, examples, exceptions).
3
Identify 10–20 “critical data elements” and define ownership, definitions, and quality checks for each.
4
Audit current state: where conflicting definitions exist (dashboards/reports) and prioritize fixes by business impact.
5
Define governance success metrics (catalog usage, glossary coverage, % of key dashboards linked to definitions, reduction in metric disputes).
6
Partner with BI and data platform leads to embed definitions into reporting and self-serve workflows.
7
Prepare interview stories that show cross-team alignment, conflict resolution, and measurable governance improvements.