Hiring a Data Engineer (AWS)?
Your team is hiring; I run the technical round for you. One 60-minute live interview per candidate, with a scored written recommendation back within 12 hours.
About the role — Moves data reliably and affordably. Pipelines are easy; correctness is not.
What you get
A 60-minute live interview with a scored written recommendation back within 12 hours. No retainer; fee quoted on the scoping call.
01 — Why this role is hard to assess
Pipeline tooling is well-trodden, so most candidates look similar on paper. The differentiator is how they handle late-arriving data, backfills and the question of what "correct" means when two systems disagree.
02 — What the 60 minutes covers
The specific ground, not a generic checklist.
- Handling late-arriving and out-of-order events
- A backfill they ran on production data, and how they verified it
- Partitioning strategy on S3, and the cost consequence of getting it wrong
- Where they would use Glue versus EMR versus plain Lambda, and why
03 — A scenario from the round
A downstream team reports yesterday’s numbers are wrong. The pipeline reported success. How do you find out what happened, and how do you stop it recurring?
Scenarios rotate between candidates so the round stays fair, and none of the published examples are used verbatim.
What I listen for
Failure modes specific to this role
- No concept of data quality checks beyond row counts
- Has never had to reprocess historical data
- Chooses tooling by familiarity rather than workload shape
- Cannot estimate what their pipeline costs to run
Scored on
- Correctness reasoning
- Pipeline design
- Cost awareness
- Debugging data issues
- Downstream empathy
Each dimension is scored with written rationale. You get the reasoning, not just the number.
Next step
Hiring a Data Engineer (AWS) this quarter?
Fifteen minutes to agree the rubric, then I run the round. One candidate or twenty — there is no retainer and no minimum.