Interview scorecard template

Data Lake Architect interview scorecard

Evaluate Data Lake Architect 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 depth in lakehouse storage formats: Delta Lake, Iceberg or Hudi, partitioning and file compaction strategy, Spark tuning, and catalogue tooling such as Glue. Use the rubric to compare role-specific evidence consistently.

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software datadata governancedata lakedelta lakespark
TL;DR
For technical proficiency, look for evidence the candidate names table formats used in production, explains compaction and Z-ordering choices, and quotes concrete query latency or storage cost effects. For systems and trade-offs, look for evidence the candidate walks through a medallion or zone design they owned, naming rejected options and the cost, latency and governance trade-offs behind each call. 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 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.

Evidence-led prompts

Interview questions for a Data Lake Architect

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

  1. 01

    Can you briefly explain your understanding of data lake architecture?

  2. 02

    Do you have experience with managed data lake services on a major cloud platform?

  3. 03

    What experience do you have developing pipelines for ingesting, processing and distributing data?

  4. 04

    How has your work on data lake architecture affected business decision making?

  5. 05

    Can you explain the concept of data lake zones such as raw, trusted and refined?

See the complete Data Lake Architect question set
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