ATS Keywords for
Data Engineers
Data engineering requisitions are written as a stack: an ingestion layer, an orchestrator, a warehouse, a transformation tool, and increasingly a streaming system. Applicant tracking systems check each layer independently, which means a CV that says "built data pipelines" without naming Airflow, dbt or Snowflake scores poorly against a posting that names all three. The vocabulary below follows that stack, so you can audit your CV layer by layer.
14 ATS keywords for Data Engineer CVs
What each term signals to the system reading your CV.
ETL / ELT
Both spellings appear in postings and mean different architectures to the teams writing them.
Apache Airflow
The default orchestration requirement, usually named explicitly rather than generically.
Snowflake / BigQuery / Redshift
Warehouse platforms are matched by name and rarely treated as interchangeable.
dbt
Now a standard transformation-layer requirement and a strong modern-stack signal.
Apache Spark
The distributed processing requirement for workloads past single-machine scale.
Python
The pipeline implementation language named in the large majority of requisitions.
SQL
Non-negotiable, and its absence from a data engineering CV reads as an error.
Kafka / streaming
Real-time ingestion is a separate competency from batch and screened as such.
Data modeling (star schema, dimensional)
Warehouse design vocabulary that separates engineers from script writers.
Data warehouse / data lake
Architecture terms that tell the screen which world you have worked in.
AWS / GCP / Azure
Cloud-specific data services (Glue, Dataflow, Synapse) make the match provider-dependent.
Data quality / validation
Increasingly written into requisitions as pipeline trust becomes a named responsibility.
CI/CD for data
Signals engineering discipline applied to pipelines rather than ad-hoc deployment.
Terraform / infrastructure as code
Appears wherever the data team owns its own infrastructure.
How to use this list
- Lay your Skills section out as the stack — ingestion, orchestration, storage, transformation — so a reviewer can map it to their own in seconds.
- Quantify the pipelines: rows or events per day, number of sources, SLA you met. Scale is the main differentiator between data engineering CVs.
- Mention data quality or observability work; it is a fast-growing requirement and still uncommon on candidate CVs.
- Name the cloud provider's own data services, not just the provider — Glue and Dataflow are separate matches from AWS and GCP.
Frequently asked questions
Everything you need to know before you upload.
Is data engineer a different keyword profile from ETL developer?
Yes, and the difference is mostly era. ETL developer postings lean toward Informatica, SSIS and stored procedures; data engineer postings lean toward Airflow, dbt and cloud warehouses. If you are moving between them, make sure the target vocabulary is present, not just the legacy one.
How do I show pipeline reliability?
With uptime or freshness numbers — pipelines delivering on a defined SLA, incidents reduced, backfills eliminated. That framing carries the data-quality keywords in context rather than as a bare list item.
Do I need Spark if my data is not that big?
Not to do the job, but its absence closes some filters. Be honest: list what you have actually run in production and lead instead with the warehouse and orchestration depth you do have.
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