Interview scorecard template

Customer Data Platform (CDP) Engineer interview scorecard

Evaluate Customer Data Platform (CDP) 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 probe identity resolution, event schema design, and the platforms and warehouses they have actually wired together. Use the rubric to compare role-specific evidence consistently.

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TL;DR
For technical proficiency, look for evidence the candidate strong on identity resolution and event schema design across the platforms and warehouse you actually run. For systems and trade-offs, look for evidence the candidate designs for event volume and late data, and names the trade-off they accepted on freshness or cost. 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

Probe identity resolution, event schema design, and the platforms and warehouses they have actually wired together.

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

Strong on identity resolution and event schema design across the platforms and warehouse you actually run.

02
Evaluation factor

Systems and trade-offs

25% weight

Test how they handle event volume, late-arriving data, and downstream systems that cannot be replayed.

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

Designs for event volume and late data, and names the trade-off they accepted on freshness or cost.

03
Evaluation factor

Evidence and rigour

25% weight

Check how they catch a broken identity graph or a silently dropped event before marketing acts on it.

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

Monitors for identity and event breakage proactively, and can describe a silent data loss they caught.

04
Evaluation factor

Collaboration and communication

15% weight

Assess how they work with marketing and privacy teams who need segments fast and consent handled correctly.

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

Works well with marketing and privacy, delivering segments while keeping consent and retention correct.

Evidence-led prompts

Interview questions for a Customer Data Platform (CDP) Engineer

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

  1. 01

    Which CDP tools and platforms have you worked with most, and which parts did you configure yourself?

  2. 02

    Have you implemented a CDP from scratch? Walk me through how the project ran.

  3. 03

    Explain what a single customer view is and why it matters in a CDP.

  4. 04

    Describe your experience with ETL or ELT processes specifically in the context of a CDP.

  5. 05

    Tell me about your experience with real-time data processing and how you decided real time was actually needed.

See the complete Customer Data Platform (CDP) Engineer question set
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