Interview scorecard template

Data Visualization Developer interview scorecard

Pre-screening scorecard for Data Visualization Developer candidates.

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software datad3jsdashboard developmentdata storytellingfront end
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 fluency in D3.js, Observable Plot or Vega-Lite plus the surrounding stack: React or Svelte, SVG and Canvas rendering, SQL against the warehouse, Tableau or Power BI extensions.

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 chart implementations built from scratch in D3 or Canvas, explains scales, axes, transitions and data joins without hesitation.

02
Evaluation factor

Systems and trade-offs

25% weight

Probe how they handle scale: rendering 500k points without freezing the browser, aggregation server side versus client side, WebGL fallbacks, caching, incremental loading in dashboards.

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

Cites concrete performance numbers and the trade-off chosen, for example switching SVG to Canvas or pre-aggregating in DuckDB to cut load time.

03
Evaluation factor

Evidence and rigour

25% weight

Assess how they validate that a chart tells the truth: axis truncation, colour scales tested for colour blindness, WCAG contrast, accessible tables, usability testing with real analysts.

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

Describes rejecting or reworking a visual because it misled readers, and cites accessibility checks or user testing that drove the change.

04
Evaluation factor

Collaboration and communication

15% weight

Look for work with analysts, product managers and non-technical stakeholders: turning vague requests into specs, running design reviews, documenting a shared chart component library.

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

Shows a repeatable intake process, gives examples of reframing a stakeholder's chart request into the question actually worth answering.

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