software datadata visualizationlod expressionstableautableau server
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 with LOD expressions, table calculations, parameter actions and data blending versus relationships; ask which SQL dialects they write and how they tune extracts.
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
Explains FIXED versus INCLUDE order of operations unprompted, writes performant custom SQL, and tunes hyper extracts with measurable load time gains.
02
Evaluation factor
Systems and trade-offs
25% weight
Probe how they structure workbooks for scale: extract refresh schedules, Tableau Server or Cloud permissions, row-level security, and when they push logic to the warehouse instead.
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 live connections against extracts with cost and freshness reasoning, and describes governance choices made across published data sources.
03
Evaluation factor
Evidence and rigour
25% weight
Assess evidence of dashboard performance work: Performance Recorder output, query counts, render times, and how they validated numbers against source systems before publishing.
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
Cites before and after load times, names the culprit (nested calcs, quick filters, cross-database joins), and shows a reconciliation process.
04
Evaluation factor
Collaboration and communication
15% weight
Look for how they extracted requirements from finance or ops stakeholders, ran adoption sessions, and handled requests for charts that would mislead.
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 redesigning a requested pie chart or gauge with a defensible alternative, plus concrete usage or adoption numbers afterwards.
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