Interview scorecard template

Data Fabric Solutions Engineer interview scorecard

Evaluate Data Fabric Solutions Engineer 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 hands-on command of virtualization and integration stacks: Denodo or Starburst/Trino query pushdown, Informatica IDMC or Talend pipelines, Unity Catalog or Collibra lineage, Kafka. Use the rubric to compare role-specific evidence consistently.

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software datadata integrationdata virtualizationmetadata catalogsolutions engineering
TL;DR
For technical proficiency, look for evidence the candidate names specific fabric components deployed, explains pushdown optimisation and cache strategy, and cites query latency figures before and after tuning. For systems and trade-offs, look for evidence the candidate weighs federation against materialisation with cost, freshness and governance evidence, and admits where a chosen fabric pattern later needed rework. 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 hands-on command of virtualization and integration stacks: Denodo or Starburst/Trino query pushdown, Informatica IDMC or Talend pipelines, Unity Catalog or Collibra lineage, Kafka streaming ingestion.

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 fabric components deployed, explains pushdown optimisation and cache strategy, and cites query latency figures before and after tuning.

02
Evaluation factor

Systems and trade-offs

25% weight

Probe architecture choices: when they virtualised versus replicated, how they handled cross-source joins, semantic layer modelling, and GDPR or residency constraints across cloud and on-prem sources.

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

Weighs federation against materialisation with cost, freshness and governance evidence, and admits where a chosen fabric pattern later needed rework.

03
Evaluation factor

Evidence and rigour

25% weight

Test how they proved a fabric deployment worked: benchmark suites, data quality rules, reconciliation against source systems, lineage completeness audits, adoption metrics per consuming team.

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

Brings measured proof: query benchmarks, reconciliation error rates, catalogue coverage percentages, and consumer adoption tracked after go-live.

04
Evaluation factor

Collaboration and communication

15% weight

Assess pre-sales and delivery interaction: running PoCs with client data teams, translating fabric concepts for stewards and CDOs, handling pushback from source system owners.

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 a PoC they scoped and demoed, names the stakeholder objections raised, and shows how architecture decisions were documented and agreed.

Evidence-led prompts

Interview questions for a Data Fabric Solutions Engineer

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

  1. 01

    What is your experience with data integration platforms?

  2. 02

    Describe your experience with data virtualisation technologies.

  3. 03

    Can you explain what a data fabric is and how it differs from traditional data management?

  4. 04

    How do you ensure interoperability between different data systems?

  5. 05

    What tools and technologies do you use for data lineage tracking?

See the complete Data Fabric Solutions Engineer question set
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