ResumeProof

Role vocabulary

ATS resume keywords for Data Engineering

These are the 24 terms our scorer checks a Data Engineering resume against. It is the same list the free check uses, published in full.

Check my resume against these Build a Data Engineering resume

The vocabulary

Coverage is 25 of the 100 points. Sixty percent of this list is full marks, so you do not need all of it, and stuffing every term in is penalised separately.

  • spark
  • pyspark
  • kafka
  • airflow
  • dbt
  • databricks
  • data pipeline
  • elt
  • data lake
  • delta lake
  • streaming
  • batch processing
  • orchestration
  • partitioning
  • sql
  • python
  • scala
  • bigquery
  • redshift
  • data quality
  • schema design
  • ingestion
  • data modelling
  • incremental load

The eight questions for Data Engineering

The guided builder asks these, in this order, and turns each answer into a line on the page. Nothing is written that you did not type.

  1. 1.How much data moves through your pipelines, in rows, events or gigabytes a day?
  2. 2.How many pipelines, tables or data products do you own?
  3. 3.What have you made faster or cheaper, and by how much?
  4. 4.Which tools, platforms and languages do you build in daily?
  5. 5.What is your data freshness or SLA, and how reliably is it met?
  6. 6.How many source systems do you integrate, and how?
  7. 7.How many analysts, teams or dashboards depend on your data?
  8. 8.What is the largest migration or platform build you have delivered?
Answer them and see your score before paying

Words are the easy half

A resume can carry every term above and still be rejected, because coverage says what you know and nothing about what you delivered. The heaviest category on the rubric is evidence: how many of your bullets carry an actual figure.

Seven in ten is full marks. Most resumes we see manage two.

Get your number free

Other functions