Interview scorecard template

Geospatial Data Engineer interview scorecard

Evaluate Geospatial Data Engineer 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 with PostGIS, GDAL/OGR, GeoPandas or Apache Sedona, plus projection handling: EPSG codes, datum shifts, and reprojecting national grids without silent geometry corruption. Use the rubric to compare role-specific evidence consistently.

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software datacoordinate systemspostgisraster processingspatial etl
TL;DR
For technical proficiency, look for evidence the candidate names specific spatial SQL functions, tiling schemes and CRS pitfalls they have hit, and explains fixes in concrete technical terms. For systems and trade-offs, look for evidence the candidate compares indexing and tiling options against query patterns and budget, citing load times or storage figures from systems they built. 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 with PostGIS, GDAL/OGR, GeoPandas or Apache Sedona, plus projection handling: EPSG codes, datum shifts, and reprojecting national grids without silent geometry corruption.

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 spatial SQL functions, tiling schemes and CRS pitfalls they have hit, and explains fixes in concrete technical terms.

02
Evaluation factor

Systems and trade-offs

25% weight

Probe how they designed pipelines for terabyte raster or LiDAR archives: partitioning by H3 or quadkey, COG versus GeoTIFF, Airflow scheduling, storage cost trade-offs.

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

Compares indexing and tiling options against query patterns and budget, citing load times or storage figures from systems they built.

03
Evaluation factor

Evidence and rigour

25% weight

Test validation habits: topology checks, geometry validity, spatial join accuracy against ground truth, and how they caught a bad ingest before downstream maps shipped.

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 automated QA on spatial data, quantifies error rates or positional accuracy, and admits a defect they detected and traced.

04
Evaluation factor

Collaboration and communication

15% weight

Assess how they work with GIS analysts, remote sensing scientists and product teams who consume tiles, APIs or dashboards built on their pipelines.

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

Translates projection and data quality constraints into plain language for analysts, and cites documentation or data contracts they maintained.

Evidence-led prompts

Interview questions for a Geospatial Data Engineer

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

  1. 01

    Can you describe a project where you built geospatial data engineering solutions?

  2. 02

    What types of geospatial engineering projects have you worked on?

  3. 03

    Have you worked with real-time geospatial data, and what did that involve?

  4. 04

    Can you describe your experience with spatial database extensions such as PostGIS?

  5. 05

    What is your experience with geospatial databases and spatial filtering?

See the complete Geospatial Data Engineer question set
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