Data Engineer Resume Example
Data Engineer resume example with pipeline architecture achievements, big data tooling, and ATS-friendly formatting tips that get callbacks.
Professional Summary Example
“Data Engineer with 6+ years of experience building and maintaining batch and streaming pipelines processing 2TB+ daily across e-commerce and fintech platforms. Expert in Spark, Airflow, dbt, and AWS data services. Rebuilt a legacy ETL stack into a lakehouse architecture that cut data delivery latency from 24 hours to 15 minutes and reduced warehouse spend by 38%.”
Experience Bullet Points
Strong bullet points that demonstrate impact with measurable results:
- Designed and deployed 40+ Airflow DAGs orchestrating ingestion from 25 sources into Snowflake, improving pipeline reliability from 92% to 99.6% on-time delivery
- Migrated 18 Spark batch jobs to structured streaming on Databricks, reducing end-to-end data latency from 6 hours to under 10 minutes for fraud detection models
- Implemented dbt-based transformation layer with 600+ tested models, cutting analyst ad-hoc SQL errors by 70% and standardizing metrics across 5 departments
- Reduced monthly Snowflake compute costs by $14K (38%) through query profiling, clustering keys, and warehouse auto-suspend policies
- Built CDC ingestion with Debezium and Kafka capturing 50M+ daily change events from PostgreSQL with exactly-once delivery guarantees
Key Skills
Languages & Querying
Data Platforms
Pipeline & Orchestration
Cloud & DevOps
Education
B.S. in Computer Science — most data engineers also list cloud certifications such as AWS Data Analytics Specialty or Google Professional Data Engineer.
Data Engineer Resume Tips
Quantify data scale everywhere — rows processed, TB stored, pipeline counts. 'Built pipelines processing 2TB daily' beats 'built data pipelines' in every recruiter scan.
Name your exact stack (Spark, Airflow, dbt, Snowflake) — data engineering screens are heavily keyword-driven and ATS filters match on specific tools.
Show cost impact. Cloud data bills are a CFO-level concern; a single '$14K/month saved' bullet differentiates you from engineers who only ship features.
Include data quality and reliability metrics (SLA %, test coverage, incident reduction) — teams hire data engineers to make data trustworthy, not just to move it.
Mention streaming experience explicitly if you have it — real-time skills (Kafka, Flink, structured streaming) command a 15-20% salary premium and are a frequent filter.
Common Mistakes to Avoid
Listing every tool ever touched — a 30-item skills dump dilutes the 8 tools that match the job description
Describing pipelines without business outcomes (what decision or product did the data power?)
Omitting orchestration and testing — raw Spark experience without Airflow/dbt context reads as scripts, not engineering
Using 'worked on' and 'helped with' instead of 'designed', 'migrated', 'reduced'
Ignoring SQL depth — advanced SQL is still the most-tested data engineering skill in interviews
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