Data · Resume guide
Data engineering resumes are screened for pipeline vocabulary — ETL/ELT, orchestration tools, and warehouse platforms by name — with scale markers (data volume, pipeline count, latency) separating builders from analysts who ran queries.
These terms appear repeatedly in real data engineer job descriptions. Use the exact forms below where your experience honestly supports them — exact matches rank; paraphrases often don't.
Share of 86 live data engineer postings in our 15 August 2026 snapshot . Front-load the ones your target job description actually names.
| Skill | Share of postings |
|---|---|
| SQL | 69% |
| Python | 54% |
| dbt | 47% |
| Snowflake | 33% |
| ETL | 30% |
| Machine Learning | 29% |
| Data Modeling | 29% |
| Airflow | 28% |
| Looker | 23% |
| Databricks | 23% |
| Spark | 23% |
| CI/CD | 21% |
Public ATS boards (Arbeitnow, Greenhouse, Ashby, Google Careers, Lever, RemoteOK, Remotive). Not LinkedIn or Indeed. Snapshot, not a forecast — always mirror the posting in front of you.
Strong data engineer bullets pair the role's vocabulary with a measured outcome. Use these as patterns — with your real numbers, never invented ones.
“Built Airflow-orchestrated ELT pipelines into Snowflake processing 3TB/day, cutting data freshness from 24 hours to 30 minutes”
“Migrated 200+ legacy SQL jobs to dbt with tests and lineage, reducing data-quality incidents 70% in two quarters”
Paste the data engineer job description you're targeting and extract its required skills and repeated phrases.
Rephrase your real experience with the posting's exact terminology — same work, their words.
Reorder your summary, skills, and top bullets so this role's priorities lead.
Verify: check your match score and remaining keyword gaps before submitting.
Name your orchestrator, warehouse, and processing framework exactly (Airflow, Snowflake, Spark) — data JDs screen on the specific modern stack. Always attach volume: '3TB/day' or '400 pipelines' is what distinguishes engineering from analysis.
The highest-weight terms for data engineer postings currently include: SQL, Python, dbt, Snowflake, ETL, Machine Learning. The definitive list is always the specific job description — paste it into a free keyword check to see exactly which terms your resume shows and which it's missing.
Extract the job description's required skills and repeated phrases, then rephrase your real experience using those exact terms — lead your summary and top bullets with what that posting emphasizes. Never add skills you don't have; keep genuine gaps visible and address them honestly.
Upload your resume, paste the job description, and get your free ATS match score with every missing keyword — then a tailored, ATS-safe rewrite without fabrication.