Data Engineering Resume Keywords: Complete ATS Reference
Data engineering keywords are increasingly screened separately from data science and software engineering as the role has become a distinct discipline. ATS systems for data engineering positions parse for specific pipeline tools, cloud data platforms, and orchestration frameworks. Listing general programming or database skills without naming the data-specific tooling leaves significant keyword gaps.
Primary Keywords
Synonym Groups
ATS systems may recognize these variations. Use the canonical form when possible, but including synonyms ensures broader matching.
ETL
Also matches: extract transform load, data integration, data ingestion
ELT
Also matches: extract load transform
Airflow
Also matches: Apache Airflow, workflow orchestration
Spark
Also matches: Apache Spark, PySpark, Spark SQL
data lake
Also matches: data lakehouse, lakehouse architecture
Related Skills
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Common Mistakes
- Listing 'ETL' without naming the specific tools used (Airflow, dbt, Fivetran, Stitch)
- Claiming 'data warehouse experience' without naming the platform (Snowflake, BigQuery, Redshift)
- Not distinguishing between batch and streaming data processing experience
- Omitting data volume metrics (rows processed, pipeline throughput, data freshness SLAs)
- Listing SQL as a skill without specifying dialect or complexity level (window functions, CTEs, recursive queries)
Optimal Resume Placement
- Technical Skills section listing pipeline tools, cloud platforms, and programming languages
- Experience bullets describing pipeline scale, data volumes, and reliability metrics
- Architecture decisions section for senior roles describing technology selection rationale
- Certifications section for cloud data credentials (Snowflake SnowPro, AWS Data Analytics)