Interview scorecard template

Data Trust Engineer interview scorecard

Evaluate Data Trust 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 depth with data quality tooling: dbt tests, Great Expectations, Soda, Monte Carlo or Anomalo, plus SQL window functions, Airflow DAGs and warehouse internals. Use the rubric to compare role-specific evidence consistently.

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software datadata observabilitydata qualitydbt testslineage
TL;DR
For technical proficiency, look for evidence the candidate names specific test suites and freshness checks they authored, explains threshold logic, and shows fluency in warehouse cost and query tuning. For systems and trade-offs, look for evidence the candidate articulates trade-offs between blocking loads and flagging anomalies, with reasoning tied to downstream consumers and SLA commitments. 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 depth with data quality tooling: dbt tests, Great Expectations, Soda, Monte Carlo or Anomalo, plus SQL window functions, Airflow DAGs and warehouse internals in Snowflake or BigQuery.

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 test suites and freshness checks they authored, explains threshold logic, and shows fluency in warehouse cost and query tuning.

02
Evaluation factor

Systems and trade-offs

25% weight

Probe how they designed contracts and lineage: schema evolution rules, upstream producer agreements, column-level lineage in OpenLineage or Atlan, and where they chose to fail a pipeline versus quarantine rows.

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

Articulates trade-offs between blocking loads and flagging anomalies, with reasoning tied to downstream consumers and SLA commitments.

03
Evaluation factor

Evidence and rigour

25% weight

Test how they measure trust: incident counts, mean time to detection, percentage of certified tables, false positive rates on anomaly alerts, and reconciliation against source systems.

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

Quotes before and after numbers on data incidents or alert precision, and describes how they validated a fix rather than assuming it worked.

04
Evaluation factor

Collaboration and communication

15% weight

Assess how they handle analysts and producers reporting broken numbers: incident comms, root cause write-ups, data dictionary ownership, and pushing schema discipline onto reluctant upstream engineering teams.

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 concrete dispute over a wrong metric, how they traced it, and how they got producers to adopt contracts without escalation.

Evidence-led prompts

Interview questions for a Data Trust Engineer

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

  1. 01

    Can you explain your experience managing data access controls and permissions?

  2. 02

    Explain your experience implementing role-based access control.

  3. 03

    Describe a project where you implemented encryption.

  4. 04

    How do you approach data classification, and why does it matter?

  5. 05

    What is your approach to data lifecycle management?

See the complete Data Trust Engineer question set
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