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The Interview Loop1 / 2

2 min lesson

Take-home / practical exercise

Explain the order in "Take-home / practical exercise", then say how you would verify the result.

Step 1 of 2

Senior data roles commonly include a four-to-eight-hour take-home and the likely shape maps straight onto the team's work: build or design a pipeline, ingestion path or data model from realistic, deliberately ambiguous requirements.

Treat it as production code you'd own on day one, not a happy-path script. The reviewers are platform engineers and they read for the instincts that keep data reliable at scale.

  1. 1Read the brief for the ambiguity, then state your assumptions. Write them down at the top - ambiguity tolerance is being graded and silent guessing reads as junior.
  2. 2Choose a partitioning and incremental-load strategy on purpose. Partition on a real query/ingest dimension, load incrementally with a watermark and say why.
  3. 3Make every transform idempotent. A re-run after a failure must not double-count; use merge/upsert semantics, not blind appends.
  4. 4Handle schema evolution and bad data. Tolerate an added column, route malformed records to a dead-letter path instead of crashing the job.
  5. 5Add data-quality checks and observability hooks. Freshness, row-count and null/distribution checks signal SLA thinking even at small scale.
  6. 6Write a README that argues trade-offs and cost. What you cut, what you assumed, what you'd do with more time and the cost implications of your layout.
The reviewer's checklist

Reviewers skim for: does it run from a clean clone, is the transform idempotent and incremental, does it survive a schema change and a malformed record, are there data-quality checks and does the README reason about cost and trade-offs. Optimize for those before any clever feature.