Technical interview guide

Data Governance Fundamentals

Who owns which data, who can access it, and how quality and compliance are enforced across an organization.

Read: 32 min
Practice MCQs: 25
Interview QA: 25
Edition: v4
Editorial status: Reviewed
Relevant for: Data Architect

Scope: UK Data Ownership Model 2026 and ESDA guidance, NIST Privacy Framework and CSF/RMF, EU GDPR, W3C DCAT 3, OpenLineage, and UK Data and AI Ethics Framework current 2026-08-31.

Interview QA

Treat each question like a live interview question: answer out loud first (structure, assumptions, tradeoffs), then open the model answer to spot gaps and rehearse a tighter follow-up.

Curated: 2026-10-05 · Written: 2026-10-05 · Reviewed: 2026-10-03

QA-1

Design a data governance operating model.

QA-2

Assign ownership for a shared customer dataset.

QA-3

Prioritize data assets for governance rollout.

QA-4

Create a data access request workflow.

QA-5

Define quality governance for a critical dataset.

QA-6

Govern secondary use of customer data.

QA-7

Design retention and deletion governance.

QA-8

Create a data-sharing agreement for an external partner.

QA-9

How do you distinguish data governance from data management, and why is governance critical to an organization?

QA-10

Implement a business glossary and catalogue.

QA-11

Govern a machine-learning training dataset.

QA-12

Resolve disagreement between a data owner and privacy officer.

QA-13

How do you define and enforce a data classification policy across an organization?

QA-14

Design governance for self-service analytics.

QA-15

Handle a critical data-definition change.

QA-16

Review a vendor data platform from a governance perspective.

QA-17

Measure whether data governance is working.

QA-18

Retire a data asset safely.

QA-19

How do you handle policy exceptions and non-compliance within a data governance framework?

QA-20

Create an exception for urgent research access.

QA-21

Respond when no owner can be found for a critical dataset.

QA-22

Test data governance before production rollout.

QA-23

Assess governance for a merged dataset.

QA-24

Design governance for data definitions and standards.

QA-25