Interview scorecard template

Data Fabric Architect interview scorecard

Evaluate Data Fabric Architect candidates across 4 weighted areas: technical proficiency, systems and trade-offs, evidence and rigour, and collaboration and communication. Technical proficiency leads at 35%, so check depth in data virtualization and catalogue tooling: Denodo, Starburst, Collibra, Purview, Informatica, plus Iceberg or Delta table formats and lineage capture through Spark. Use the rubric to compare role-specific evidence consistently.

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software datadata fabricdata meshdata virtualizationmetadata management
TL;DR
For technical proficiency, look for evidence the candidate names specific engines and catalogues they configured, explains active metadata, lineage harvesting and semantic layer modelling without hiding behind vendor slides. For systems and trade-offs, look for evidence the candidate argues both sides with cost and latency numbers, admits where virtualization failed and describes the fallback pipeline they built instead. 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 proficiency

35% weight

Check depth in data virtualization and catalogue tooling: Denodo, Starburst, Collibra, Purview, Informatica, plus Iceberg or Delta table formats and lineage capture through Spark or dbt.

Evidence to listen for

  • Command of the languages, frameworks, and data tools the role actually uses
  • Understands correctness, performance, and failure modes, not just syntax
  • Has opinions on testing and can justify them
  • Reads and reasons about code they did not write

Five-point scoring guide

1
Poor

Cannot work independently; fundamentals are missing.

2
Needs Improvement

Weak fundamentals; output needs heavy review.

3
Satisfactory

Competent for the role; needs guidance on complex or unfamiliar work.

4
Very Good

Strong practitioner; handles hard problems with little guidance.

5
Excellent

Names specific engines and catalogues they configured, explains active metadata, lineage harvesting and semantic layer modelling without hiding behind vendor slides.

02
Evaluation factor

Systems and trade-offs

25% weight

Probe architectural trade-offs they chose: federated query versus physical replication, centralised warehouse versus domain-owned mesh products, and how they handled latency, egress cost and data gravity.

Evidence to listen for

  • Reasons about scale, latency, cost, and failure before writing code
  • Names the trade-off they chose and what they gave up
  • Understands the data lifecycle end to end
  • Anticipates what breaks at ten times the volume

Five-point scoring guide

1
Poor

No thinking beyond the immediate task; no awareness of scale or failure.

2
Needs Improvement

Limited architectural awareness; struggles with design decisions.

3
Satisfactory

Works within an existing design; makes sound local decisions.

4
Very Good

Designs for scale and maintainability; articulates trade-offs clearly.

5
Excellent

Argues both sides with cost and latency numbers, admits where virtualization failed and describes the fallback pipeline they built instead.

03
Evaluation factor

Evidence and rigour

25% weight

Assess evidence behind their fabric: query performance benchmarks, catalogue coverage percentages, time-to-onboard a new data product, policy enforcement audits against GDPR or HIPAA rules.

Evidence to listen for

  • Validates results rather than trusting output
  • Knows how their work is measured and what a bad result looks like
  • Can describe a time their own analysis or model was wrong and how they caught it
  • Careful about data quality, leakage, and silent failure

Five-point scoring guide

1
Poor

Ships unvalidated work; no notion of how correctness is checked.

2
Needs Improvement

Validates superficially; misses obvious quality or leakage issues.

3
Satisfactory

Reasonable checks in place; rigour drops under time pressure.

4
Very Good

Validates thoroughly; can name a real error they caught in their own work.

5
Excellent

Cites before and after metrics such as discovery time cut from weeks to days, with named workloads and how measurement was instrumented.

04
Evaluation factor

Collaboration and communication

15% weight

Test how they aligned domain data owners, platform engineers and governance councils; look for data contracts, stewardship models and RACI decisions they drove without formal authority.

Evidence to listen for

  • Explains technical work to non-technical stakeholders
  • Gives and takes code or peer review constructively
  • Documents enough that the work survives their absence
  • Aligns with team process rather than working around it

Five-point scoring guide

1
Poor

Cannot work in a team; resistant to feedback.

2
Needs Improvement

Communication issues create rework; lone-wolf tendencies.

3
Satisfactory

Adequate team member; documentation and review participation are light.

4
Very Good

Communicates well; reliable reviewer and collaborator.

5
Excellent

Describes converting reluctant domain teams into product owners, citing contract templates, review forums and one dispute they resolved concretely.

Evidence-led prompts

Interview questions for a Data Fabric Architect

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

  1. 01

    Can you provide examples of the data fabric projects you have worked on?

  2. 02

    What is your experience designing and implementing data architecture solutions?

  3. 03

    Can you explain a complex data architecture you have developed?

  4. 04

    Describe a scenario where you used data virtualisation to unify data.

  5. 05

    How would you handle the load of massive data sources in real time?

See the complete Data Fabric Architect question set
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