Get your data engineer resume past ATS screening. Paste any job description below, get your keyword match score, and generate a tailored CV in 60 seconds.
These keywords appear most frequently in data engineer job descriptions. Missing even a few can drop your ATS score below the screening threshold.
Hard and soft skills that data engineer ATS systems look for
Common mistakes that cause data engineer resumes to fail ATS screening
List 'dbt' explicitly - it's become a required ATS keyword in 70%+ of modern data engineering JDs
Include both 'ETL' and 'ELT' - modern data stacks favor ELT but ATS scans for both separately
Quantify pipeline scale: 'built ELT pipeline processing 50TB/day', 'reduced data latency from 8 hours to 15 minutes'
Name your orchestration tool: 'Apache Airflow', 'Prefect', or 'Dagster' - ATS treats each as a distinct keyword
Include 'data modeling' and the specific approach ('star schema', 'Data Vault') - architects and analytics engineers look for this
Add 'data quality', 'Great Expectations', or 'dbt tests' - data reliability is increasingly an ATS filter for senior DE roles
The modern data stack that dominates 2024 JDs: Python + SQL + dbt + Airflow + Snowflake or BigQuery + Spark/Databricks. Cloud platform preference varies: AWS (Glue, S3, Athena), GCP (Dataflow, BigQuery), or Azure (ADF, Synapse Analytics). Kafka or Kinesis for streaming. Delta Lake or Iceberg for lakehouse architecture. Use ATS CV Checker to match your stack against specific JDs.
Data engineers build and maintain data infrastructure - pipelines, warehouses, and streaming systems. Data scientists build models and analyze data. Data engineer resumes should emphasize: pipeline tools (Airflow, Spark, dbt), warehouse technologies (Snowflake, BigQuery), data reliability, and infrastructure. Avoid leading with machine learning unless the role is explicitly a 'Data Scientist/Engineer' hybrid.
Be specific: 'built 150+ dbt models powering analytics for 8 business domains', 'implemented dbt tests reducing data quality incidents by 60%', 'designed modular dbt project with staging/intermediate/mart layers'. List related keywords: dbt, dbt Cloud, Jinja, Snowflake (as dbt target), dbt tests (singular, generic, custom). The dbt Certified Developer certification is a strong ATS signal.
Yes, in most cases. Even if you primarily use Snowflake or BigQuery for warehousing, Spark/PySpark and Databricks experience is an ATS filter for 60-70% of senior data engineering JDs. It signals you can handle large-scale distributed data processing. If you haven't used Spark, prioritize Databricks Certified Associate Developer certification and a side project demonstrating PySpark.
Create separate subsections or clearly label each role's scope. For batch: mention Airflow/dbt/Spark and data warehouse work. For streaming: mention Kafka, Kinesis, Flink, or Spark Streaming with latency metrics. If you have both, explicitly state 'batch and streaming pipelines' in your Summary - this is a premium differentiator. Include 'real-time data' and 'event-driven architecture' as separate ATS keywords.
Guides to help you pass ATS screening faster