What this role’s resume is actually judged on — the language postings use, what a reviewer looks for in the first ten seconds, and the specific way Data Engineer resumes go wrong.
A first pass takes seconds and is looking for three things. If they are not near the top, the rest of the page rarely gets read.
Most resumes fail one bullet at a time. The difference is almost always specificity — a number, a constraint, or a consequence.
Built and maintained ETL pipelines using Airflow.
Rebuilt the nightly ingest to be idempotent and backfillable, ending a recurring class of silent wrong numbers that had corrupted three months of revenue reporting.
Terms that recur in real postings for this role. They belong in the bullet that proves them, not in a list at the bottom — our own checker weights requirement coverage at 40% and raw keyword matching at 20%, and most serious systems make a similar trade.
A keyword you cannot defend in an interview is worse than a missing one. How the format checks work.
Anyone can build a pipeline that works on a good day. Data engineering is judged on late data, schema drift, backfills and silent corruption — a resume that never mentions failure reads as someone who has not run anything in production.
Pipelines, not models. Expect idempotency, late-arriving data, backfills and schema evolution — and at least one question about what happens when a job silently produces wrong numbers for a week.
AUC where the business metric should be
Listing tools instead of questions answered
Research projects presented as production work
No evaluation story anywhere on the page
Reading as an analyst who happens to write dbt
Listing technologies you have touched once
Generic resume advice only goes so far — what matters is whether this resume covers this posting. Paste both and get requirement-by-requirement coverage, the keywords you are missing, and the format problems a parser will hit. The first check is free.