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ATS Keywords for Data Engineer Resumes

Data engineering resumes live or die on pipeline reliability and scale — the volume of data moved, the latency of the pipeline, and how it survived schema changes and upstream failures.

Hard Skills

ETL/ELT PipelinesData WarehousingSQLDistributed Processing (Spark)Data ModelingBatch & Streaming Pipelines

Soft Skills

Cross-functional Data ContractsData Quality OwnershipStakeholder Communication

Tools & Certifications

Apache AirflowSnowflake/BigQuery/RedshiftdbtApache KafkaPythonAWS/GCP Data Engineer Cert

How to use these keywords

State the data volume and pipeline latency you worked with (TB/day, near-real-time vs. batch) — scale is the core signal here.
Name the specific warehouse and orchestration tools (Snowflake, Airflow, dbt) — generic "data pipeline" language gets filtered out.
Mention data quality/validation work explicitly — it separates senior data engineers from script writers.

See recommended resume templates for Data Engineer

Curated templates that pair well with this role's keyword profile.

Frequently asked questions

Data engineer vs. data analyst — which resume approach fits me?

If you build and maintain the pipelines others query, you're a data engineer — lead with infrastructure and scale, not analysis or dashboards.

Should I list Spark if I only used managed services built on it?

Yes, but be specific ("Spark via Databricks") — it's accurate and still matches ATS searches for "Spark."

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