Data Engineer Resume Example & Writing Guide
View a professional data engineer resume example with proven bullet points, key skills, and expert tips. Copy what works and customize with your own experience.
Professional Summary Examples
Start your resume with a compelling summary. Here are proven examples you can adapt:
Data engineer with 5+ years of experience building scalable data pipelines and warehouse architectures. Designed ETL infrastructure processing 10TB+ daily across 50+ data sources. Expert in Spark, Airflow, and modern data stack (dbt, Snowflake, Fivetran).
Performance-driven data engineer specializing in real-time streaming and big data processing. Built event-driven pipeline handling 1M+ events per second using Kafka and Spark Streaming. Strong background in data modeling, quality frameworks, and cost optimization.
Data engineer with expertise in cloud-native data platforms and DataOps practices. Migrated legacy data warehouse to Snowflake, reducing query times by 80% and infrastructure costs by 50%. Passionate about data quality, lineage, and governance.
Work Experience Bullet Points
Use these achievement-focused bullet points as inspiration. Replace the numbers with your own metrics.
- Designed and built data pipeline infrastructure processing 10TB+ daily from 50+ sources using Airflow, Spark, and S3
- Built real-time streaming pipeline using Kafka and Spark Streaming, processing 1M+ events per second with sub-second latency
- Migrated legacy on-premises data warehouse to Snowflake, reducing query times by 80% and infrastructure costs by 50%
- Implemented dbt transformation layer with 200+ models, establishing testing and documentation standards for analytics engineering
- Designed dimensional data model for enterprise data warehouse supporting 100+ downstream analytics consumers
- Built automated data quality framework catching 95% of data issues before they impacted downstream reports and dashboards
- Reduced Spark job processing time by 60% through partition optimization, caching strategies, and cluster right-sizing
- Created self-service data ingestion platform enabling 20+ teams to onboard new data sources without engineering support
- Implemented data lineage tracking using Apache Atlas, providing end-to-end visibility across 500+ data assets
- Led migration of 100+ Airflow DAGs from on-premises to Managed Airflow (MWAA), achieving 99.9% pipeline reliability
Key Skills for Data Engineer Resume
Include these skills on your resume to pass ATS screening and impress recruiters:
Recommended Certifications
These certifications can strengthen your data engineer resume:
Tips for Your Data Engineer Resume
- Quantify your achievements: Use specific numbers, percentages, and dollar amounts to demonstrate impact.
- Use industry keywords: Include terms from the job description to pass ATS screening.
- Lead with action verbs: Start bullet points with strong verbs like developed, implemented, increased, reduced.
- Keep it concise: Aim for one page unless you have 10+ years of relevant experience.
Frequently Asked Questions
What skills are most important for a data engineer resume?
SQL and Python are foundational. Include big data tools (Spark, Kafka), orchestration (Airflow, Dagster), cloud data platforms (Snowflake, BigQuery, Redshift), transformation tools (dbt), and data modeling experience. Show expertise in both batch and streaming processing paradigms.
How do I differentiate a data engineer resume from a data analyst resume?
Focus on infrastructure and engineering: pipeline architecture, data modeling, scalability, performance optimization, and system design. Emphasize volumes of data processed, pipeline reliability metrics, and infrastructure you built. Data engineers build the systems; data analysts use them.
Should data engineers include software engineering skills on their resume?
Yes. Modern data engineering requires strong software engineering fundamentals: version control, testing, CI/CD, containerization, and clean code practices. Include experience with Python packaging, unit testing for data pipelines, and infrastructure as code.
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