Tailor your CV for Data Engineers roles
Data engineering postings name their stack — Spark, Airflow, dbt, Snowflake or BigQuery — and ATS filters cut CVs that say 'ETL experience' without the tools. Recruiters who pass you through look for scale (terabytes processed, events per day) and reliability (SLA adherence, pipeline failure rates), because anyone can schedule a script; few can run data platforms teams depend on.
Tailor to the company's ecosystem: dbt and warehouse modeling for analytics-engineering-flavored roles, Kafka and streaming for real-time platforms, Spark tuning for big-data shops. Quantify ruthlessly — 'rebuilt nightly ETL into incremental dbt models, cutting warehouse cost 45% and refresh time from 6 hours to 40 minutes' — and show data quality ownership: testing, lineage, observability.
Key skills recruiters look for
CV tips that actually move the needle
State data volume and pipeline scale
'Built data pipelines' is meaningless without size. Write 'Operated Airflow DAGs ingesting 2TB/day from 40 sources into BigQuery with 99.9% SLA'. Volume, source count, and reliability numbers are how data hiring managers calibrate seniority instantly.
Show warehouse cost and performance tuning
Cloud warehouse bills are a leadership pain point. Bullets like 'Cut Snowflake credits 35% via clustering keys and query optimization' or 'Partitioned BigQuery tables, reducing scan costs 60%' prove business awareness most data engineer CVs lack.
Include data quality and testing practices
Name dbt tests, Great Expectations, data contracts, or lineage tooling, with an outcome: 'Added 300 dbt tests and freshness alerts, cutting data incidents from weekly to under one per month'. Quality ownership separates platform engineers from script writers.
Mirror the posting's stack and modeling terms
If the requisition says dbt, Snowflake, and dimensional modeling, those exact strings belong in your CV where true — not 'transformation tooling' or 'cloud warehouse'. Add orchestration (Airflow, Dagster) and streaming (Kafka, Kinesis) terms only when you genuinely ran them.
Frequently asked questions
What keywords do ATS scan for in data engineer CVs?
SQL, Python, Spark, Airflow, dbt, Kafka, and the posting's warehouse — Snowflake, BigQuery, Redshift, or Databricks — plus 'ETL', 'ELT', 'data modeling', and 'data pipelines'. Cloud platform terms (AWS Glue, GCP Dataflow) matter when listed. Match exact tool names; parsers treat 'Air flow' and 'Airflow' differently.
How long should a data engineer CV be?
One page under eight years of experience; two for senior or platform-lead scope. Open with a stack line (orchestration, warehouse, transformation, streaming) and one scale metric. Hiring managers skim for tools, volume, and reliability numbers — long prose paragraphs about responsibilities get skipped entirely.
How do I move from analytics or backend into data engineering on paper?
Reframe existing work in data-platform terms: SQL models you productionized, pipelines you scheduled, warehouse tables you owned. Add one substantial portfolio project — an end-to-end pipeline with Airflow, dbt, and a cloud warehouse, linked on GitHub — and lead skills with the target stack rather than your old title's tools.