Evaluate Data Visualization Developer 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 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. Use the rubric to compare role-specific evidence consistently.
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TL;DR
For technical proficiency, look for evidence the candidate names specific chart implementations built from scratch in D3 or Canvas, explains scales, axes, transitions and data joins without hesitation. For systems and trade-offs, look for evidence the candidate cites concrete performance numbers and the trade-off chosen, for example switching SVG to Canvas or pre-aggregating in DuckDB to cut load time.
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 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.
Evidence-led prompts
Interview questions for a Data Visualization Developer
Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.
01
What are some of the major projects that have featured your visualisation work?
02
What platforms have you worked on, and what makes you comfortable with them?
03
What are your technical proficiencies with regard to visualisation tools?
04
What do you consider the most important aspect of a good visualisation?
05
How do you translate complex data into a format non-technical people understand?
