Data Engineer resume guide

This guide helps you tailor a Data Engineer resume to a specific job description while keeping it clear, truthful, and ATS-friendly.

What hiring teams look for

• Building reliable data pipelines (batch + streaming)
• Data modeling and transformation (warehouse/lakehouse)
• Ownership, monitoring, and on-call mindset
• Performance, cost, and reliability improvements

Strong resume structure

• Header (name, location, links)
• 2–3 line summary aligned to the role
• Skills (grouped, not a keyword dump)
• Experience (impact-first bullets)
• Projects (optional but powerful)
• Education / certifications (as relevant)

Skills section: what to include

• Core: SQL, Python, ETL/ELT
• Orchestration: Airflow (or equivalent)
• Warehousing: Snowflake/BigQuery/Redshift (only what you used)
• Modeling: dbt (if relevant)
• Cloud: AWS/GCP/Azure (services you actually touched)
• Observability: logging, alerting, SLAs

Bullet writing: the formula that works

Use: Action + Method + Result (+ Scope)

Examples:

• “Built Airflow DAGs in Python to load Snowflake models via dbt, reducing refresh time from 3h to 40m.”
• “Designed incremental models and partitioning strategy that cut warehouse cost by ~22%.”
• “Implemented data quality checks and alerts, reducing incidents by 50% quarter-over-quarter.”

ATS and formatting notes

• Keep headings standard (Experience / Skills / Education)
• Prefer consistent date formats and clear job titles
• Avoid complex tables for core content

Common pitfalls

• Listing every tool you’ve heard of
• Bullets that describe duties without outcomes
• No mention of scale (rows/day, jobs/day, stakeholders served)

Using HyperApply for this role

• Use HyperApply to tailor your summary and first 1–2 roles to each job posting.
• Keep your base CV as the truth; review the generated bullets for accuracy.
• Related: /docs/how-to-generate-a-tailored-cv-from-a-job-post, /learn/how-to-quantify-impact-in-resume-bullets