Interview scorecard template

Data Governance Manager interview scorecard

Evaluate Data Governance Manager candidates across 4 weighted areas: technical depth, real incidents and findings, risk judgement, and getting things fixed. Technical depth leads at 35%, so check depth on cataloguing and lineage tooling: Collibra, Alation or Purview, DAMA-DMBOK domains, master data matching rules, retention schedules, and how they defined data quality. Use the rubric to compare role-specific evidence consistently.

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TL;DR
For technical depth, look for evidence the candidate names specific catalogue and lineage implementations, quality rule sets they authored, and reference or master data models they governed end to end. For real incidents and findings, look for evidence the candidate recounts named incidents with root cause, affected systems and reports, remediation steps, and the control that stopped recurrence. Apply the written 1–5 anchors to every answer, record the evidence behind each rating, and use the factor weights to reach a consistent overall assessment.
Complete evaluation framework

What to assess and how to score it

Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.

01
Evaluation factor

Technical depth

35% weight

Check depth on cataloguing and lineage tooling: Collibra, Alation or Purview, DAMA-DMBOK domains, master data matching rules, retention schedules, and how they defined data quality dimensions and thresholds.

Evidence to listen for

  • Command of the specific attack surface, tooling, and controls the role covers
  • Understands how the underlying system works, not just how the tool reports on it
  • Can explain an attack or control chain end to end
  • Distinguishes what they found themselves from what a scanner flagged

Five-point scoring guide

1
Poor

Tool operator only; no understanding of the systems underneath.

2
Needs Improvement

Runs tooling but cannot explain findings or how the attack works.

3
Satisfactory

Solid working knowledge; depth thins outside familiar tooling.

4
Very Good

Strong command of the domain; explains attack and control chains clearly.

5
Excellent

Names specific catalogue and lineage implementations, quality rule sets they authored, and reference or master data models they governed end to end.

02
Evaluation factor

Real incidents and findings

30% weight

Probe actual governance failures they handled: a DSAR they could not fulfil, a broken lineage feeding a regulatory report, duplicate customer records, or a failed audit finding.

Evidence to listen for

  • Brings specific incidents, findings, or audits they personally worked
  • States their own role rather than the team's
  • Describes what was actually at risk and what changed afterwards
  • Can talk about a finding that turned out to be wrong

Five-point scoring guide

1
Poor

No hands-on work; knowledge is entirely certification or coursework.

2
Needs Improvement

Limited exposure; cannot describe their contribution to an incident.

3
Satisfactory

Real casework with adequate detail; ownership sometimes vague.

4
Very Good

Specific incidents with clear personal scope and what changed after.

5
Excellent

Recounts named incidents with root cause, affected systems and reports, remediation steps, and the control that stopped recurrence.

03
Evaluation factor

Risk judgement

20% weight

Assess how they rank data risk: classification tiers, critical data element selection, GDPR or CCPA exposure, cross-border transfers, and where they accepted risk rather than blocking a release.

Evidence to listen for

  • Prioritises by actual exploitability and business impact, not raw severity scores
  • Can argue for accepting a risk as well as fixing it
  • Knows the difference between a finding and a problem
  • Does not cry wolf or wave things through

Five-point scoring guide

1
Poor

Treats every finding as critical, or waves real risk through.

2
Needs Improvement

Follows severity scores mechanically; no business context.

3
Satisfactory

Reasonable prioritisation; less confident arguing for risk acceptance.

4
Very Good

Prioritises by exploitability and impact; can justify accepting a risk.

5
Excellent

Prioritises by regulatory and business impact, justifies documented risk acceptances, and distinguishes critical data elements from noise.

04
Evaluation factor

Getting things fixed

15% weight

Look for evidence they moved stewards and engineers to act: data owner appointments, governance council agendas, policy adoption rates, quality scorecards trending up, issues closed in the log.

Evidence to listen for

  • Writes findings engineers can act on rather than a wall of output
  • Has persuaded a team to fix something they did not want to fix
  • Explains risk to executives in business terms
  • Works with the org rather than policing it

Five-point scoring guide

1
Poor

Adversarial with engineering; findings never get fixed.

2
Needs Improvement

Reports are unactionable; no influence beyond raising tickets.

3
Satisfactory

Adequate reporting; relies on mandate rather than persuasion.

4
Very Good

Actionable findings and a real record of getting fixes shipped.

5
Excellent

Shows named owners recruited, council decisions logged, and measurable movement in quality scores or open issue backlog.

Evidence-led prompts

Interview questions for a Data Governance Manager

Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.

  1. 01

    Can you tell me about your previous experience managing data governance?

  2. 02

    Can you provide an example of a successful data governance project you have managed?

  3. 03

    Can you provide an example of a data governance policy you have created and implemented?

  4. 04

    How do you ensure data quality in your governance efforts?

  5. 05

    How do you assess the effectiveness of a data governance strategy?

See the complete Data Governance Manager question set
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