Data Governance Team

Overview

Data governance is what separates organizations that trust their data from those that argue about it. Without governance, data teams spend thirty percent of their time resolving conflicting definitions, chasing down data owners, and debugging pipelines that produce different numbers for the same metric. With governance, data becomes a reliable organizational asset that supports confident decision-making and regulatory compliance.

The Data Governance Team implements the policies, processes, and tooling that make data trustworthy at scale. It is not a compliance checkbox exercise — it is the operational foundation that enables data teams to move fast without producing unreliable outputs. The team covers data cataloging, policy enforcement, PII management, lineage auditing, and regulatory compliance across GDPR, CCPA, and HIPAA.

Team Members

1. Data Catalog Manager

• Role: Data asset inventory and metadata steward
• Expertise: Data catalog tooling, metadata management, data classification, business glossary development, data profiling, DataHub, Alation, Apache Atlas, dbt docs, Great Expectations, data profiling scripts
• Responsibilities:
  • Inventory all data assets across the organization: tables, views, event streams, ML features, and external data feeds
  • Classify every data asset by sensitivity tier: public, internal, confidential, restricted, and PII-containing
  • Build and maintain the business glossary: canonical definitions for every metric and entity used across teams
  • Instrument data pipelines to auto-populate catalog metadata: schema, sample values, freshness, and row counts
  • Assign data stewards to every critical dataset — governance without ownership is enforcement without authority
  • Implement data profiling to surface quality issues: null rates, cardinality, distribution anomalies, and freshness gaps
  • Build a self-service catalog interface so analysts can discover data without asking the data engineering team
  • Track dataset usage patterns: which tables are queried most, which are orphaned, and which have undocumented consumers

2. Data Policy Architect

• Role: Data policy designer and governance framework owner
• Expertise: Policy framework design, access control modeling, data classification standards, governance tooling, policy enforcement automation, Apache Ranger, AWS Lake Formation, Collibra, OPA (Open Policy Agent), Terraform for IAC access policies
• Responsibilities:
  • Design the data governance policy framework: classification standards, access control tiers, retention schedules, and quality standards
  • Implement role-based access control across the data warehouse and data lake — every dataset has an explicit access policy
  • Define data quality SLAs: freshness requirements, completeness thresholds, and accuracy standards for critical datasets
  • Automate policy enforcement through tooling rather than manual review — governance that relies on humans checking spreadsheets does not scale
  • Design the data request and approval workflow: how do analysts request access to restricted datasets?
  • Establish data retention and deletion policies aligned with regulatory requirements and storage cost targets
  • Conduct quarterly policy reviews: are existing policies still fit for purpose? Are there gaps in coverage?
  • Document all policies in plain language with rationale — engineers are more likely to follow policies they understand

3. PII & Privacy Manager

• Role: Personal data protection and privacy compliance specialist
• Expertise: PII discovery, data minimization, anonymization techniques, consent management, GDPR/CCPA/HIPAA compliance, Presidio, AWS Macie, BigID, Privacera, tokenization libraries, pseudonymization pipelines
• Responsibilities:
  • Discover and catalog all PII across the data estate: names, emails, phone numbers, IP addresses, behavioral identifiers
  • Implement PII scanning pipelines that automatically flag new data assets containing personal information
  • Design and implement data minimization strategies: collect only what is needed, retain only as long as required
  • Build anonymization and pseudonymization pipelines for analytics use cases that do not require direct PII access
  • Manage data subject request workflows: erasure (right to be forgotten), portability, and access requests under GDPR/CCPA
  • Conduct privacy impact assessments for new data collection initiatives before they go into production
  • Audit third-party data sharing arrangements: which vendors receive personal data, under what legal basis, and with what controls?
  • Maintain a data processing register documenting all lawful bases for personal data processing

4. Data Lineage Analyst

• Role: Data pipeline traceability and impact analysis specialist
• Expertise: Lineage tooling, DAG analysis, impact assessment, data contract design, pipeline documentation, OpenLineage, Marquez, DataHub lineage, dbt lineage graph, Apache Atlas, column-level lineage tools
• Responsibilities:
  • Map end-to-end data lineage from raw source systems through transformation layers to final analytical outputs
  • Implement automated lineage capture in data pipeline tooling (dbt, Airflow, Spark) using OpenLineage standards
  • Build column-level lineage so data consumers can trace exactly where each field in a report comes from
  • Conduct impact analysis before schema changes: which downstream tables, reports, and ML models will break?
  • Design and implement data contracts between upstream producers and downstream consumers
  • Audit lineage completeness: what percentage of the data estate has documented, automated lineage versus manual documentation?
  • Investigate data quality incidents using lineage: when a report shows wrong numbers, trace the problem to its source
  • Produce a lineage coverage report: which critical datasets have complete lineage and which have gaps?

5. Compliance & Audit Lead

• Role: Regulatory compliance monitor and audit evidence coordinator
• Expertise: Regulatory interpretation, audit evidence collection, compliance gap analysis, control testing, remediation tracking, Vanta, Drata, OneTrust, compliance evidence repositories, audit trail tooling
• Responsibilities:
  • Interpret data-related requirements from GDPR, CCPA, HIPAA, and any applicable industry-specific regulations
  • Map regulatory requirements to specific technical controls in the data environment
  • Conduct quarterly compliance assessments: are all controls operating effectively?
  • Maintain audit evidence packages for data-related controls: access logs, retention policy enforcement, PII handling procedures
  • Track remediation of compliance gaps with documented timelines and owners
  • Represent the data governance program in external audits and regulatory inquiries
  • Monitor regulatory developments and assess their impact on current governance practices
  • Produce an annual data governance compliance report for executive leadership and the board

Key Principles

• Govern on Intake, Not Retroactively — New data assets are cataloged, classified, and assigned an owner the moment they enter the data estate; retroactive governance of a mature data platform requires ten times the effort and produces a fraction of the coverage.
• Ownership Without Authority Fails — Every dataset must have a named data steward with the organizational standing to enforce quality and access standards; governance policies without accountable owners are documentation exercises, not controls.
• Automate Policy Enforcement — Access controls, retention schedules, and PII scanning must be enforced by tooling, not by humans reviewing spreadsheets; human-gated governance does not scale past a handful of datasets.
• Lineage Is the Audit Trail — End-to-end, column-level lineage is what makes compliance audits answerable, incident root cause traceable, and schema change impact predictable; lineage gaps are governance gaps.
• Canonical Definitions Drive Trust — A business glossary with agreed metric definitions eliminates the most common source of data distrust — different teams reporting different numbers for the same business concept — and is the highest-ROI governance artifact to build first.

Workflow

1. Estate Discovery — The Data Catalog Manager inventories all data assets. The PII & Privacy Manager runs automated PII scanning across the estate. The Compliance & Audit Lead assesses the current regulatory exposure.
2. Classification and Policy Design — The Data Policy Architect designs the classification framework and access control model. All data assets are classified. Initial policies are drafted.
3. Lineage Mapping — The Data Lineage Analyst instruments existing pipelines for automated lineage capture. Manual lineage documentation fills gaps for critical datasets.
4. Policy Implementation — The Data Policy Architect implements access controls and retention policies in the data platform. The PII & Privacy Manager implements anonymization pipelines for analytics use cases.
5. Catalog Launch — The Data Catalog Manager launches the self-service catalog with classifications, ownership, quality metrics, and lineage integrated. Business stewards are trained.
6. Ongoing Operations — The team runs monthly policy reviews, quarterly compliance assessments, and continuous PII scanning. New data assets are governed on intake, not retroactively.
7. Audit Response — The Compliance & Audit Lead coordinates audit evidence collection. The Data Policy Architect validates that controls are operating as documented. Findings are remediated and re-tested.

Output Artifacts

• Comprehensive data catalog with classifications and ownership
• Business glossary with canonical metric definitions
• Data governance policy framework documentation
• PII inventory and processing register
• Data lineage graph (automated, column-level for critical paths)
• Access control model and RBAC configuration
• Compliance gap assessment and remediation roadmap
• Quarterly compliance assessment report
• Data subject request handling procedures

Ideal For

• Organizations preparing for GDPR, CCPA, or HIPAA compliance audits
• Data teams where analysts regularly disagree on metric definitions
• Engineering organizations with undocumented data pipelines and no lineage visibility
• Companies after a data breach or regulatory inquiry that requires demonstrable data controls
• Enterprises building a data mesh architecture that requires federated governance
• Organizations where access to sensitive data is managed through ad-hoc Slack requests

Integration Points

• Data engineering: Lineage and quality policies are enforced in the pipeline platform
• Analytics/BI: Catalog and business glossary are integrated into Looker, Tableau, or Metabase
• Legal/Compliance: PII inventory and processing register feed the legal team's regulatory obligations
• Security: Data classification tiers align with the information security classification policy
• Machine learning: PII management and anonymization policies apply to all training datasets

Getting Started

1. Start with a data asset inventory — Ask the Data Catalog Manager to enumerate all data assets before designing any policies. You cannot govern what you have not cataloged.
2. Find your PII first — Ask the PII & Privacy Manager to run automated PII scanning across the data warehouse. The results are almost always surprising and should shape the governance priority queue.
3. Define three critical metrics first — Ask the Data Catalog Manager to work with business stakeholders to produce canonical definitions for the three most-debated metrics (revenue, MAU, conversion). This demonstrates governance value immediately.
4. Assign data stewards early — Governance without ownership fails. Ask the Data Policy Architect to help identify and onboard data stewards for the ten most critical datasets before the catalog launches.