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 depth in lakehouse storage formats: Delta Lake, Iceberg or Hudi, partitioning and file compaction strategy, Spark tuning, and catalogue tooling such as Glue or Unity Catalog.
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 table formats used in production, explains compaction and Z-ordering choices, and quotes concrete query latency or storage cost effects.
02
Evaluation factor
Systems and trade-offs
25% weight
Test how they zoned raw, curated and serving layers, chose batch versus streaming ingestion, and weighed warehouse offload against keeping compute on the lake.
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
Walks through a medallion or zone design they owned, naming rejected options and the cost, latency and governance trade-offs behind each call.
03
Evaluation factor
Evidence and rigour
25% weight
Assess evidence of data quality enforcement: schema evolution rules, Great Expectations or dbt tests, lineage tracking, GDPR deletion handling, and measured pipeline reliability figures.
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 freshness SLAs, failed-load rates, lineage coverage and specific quality gates that blocked bad data before consumers saw it.
04
Evaluation factor
Collaboration and communication
15% weight
Look for how they aligned analysts, ML engineers and platform teams on contracts, access control models, and migration timelines away from legacy Hadoop or extract-based reporting.
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 negotiating data contracts and access tiers with named consumer teams, plus documentation or standards others in the org adopted.
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