Data Engineer Resume Example and Template
A strong Data Engineer resume demonstrates expertise building scalable data infrastructure and pipelines. Show the volume of data you've processed, pipeline reliability you've achieved, and architectural decisions that enabled analytics or AI teams to succeed.
Recruiters seek engineers who understand both data systems and software engineering practices. Mention ETL frameworks, distributed processing, schema design, and the business outcomes enabled by your pipelines.
A complete, realistic Data Engineer resume, laid out in an ATS-tested template. The candidate is illustrative: the name, employers and figures were written for this example and identify no real person, so you can copy its structure and wording freely.
Updated September 2026
Also searched as
Senior Data Engineer
Nathan Wright: Data Engineer
Denver, Colorado, United States
Summary
Infrastructure-focused Data Engineer with 6 years designing and scaling data pipelines. Expert in Spark, Airflow, Python, and cloud data warehouses. Built ETL systems processing 450M+ daily records serving 28 analytics teams. Reduced pipeline latency by 64% through optimization and improved data availability from 94.2% to 99.8%.
Work Experience
Senior Data Engineer, DataScale Platforms
Aug 2022 – Present. 120-person data infrastructure company providing managed data pipelines for 35 enterprise customers
- Architected data warehouse migration from legacy Teradata to Snowflake for 8 customers, processing 450M+ daily records while improving query performance by 3.2x and reducing infrastructure costs by 41%.
- Designed and implemented comprehensive data quality framework using Great Expectations, improving data reliability from 94.2% to 99.8% and reducing data-driven errors by 87%.
- Led optimization initiative on Airflow-based ETL platform, reducing average pipeline latency by 64% through parallel processing and caching strategies, enabling real-time reporting for product teams.
- Mentored 4 junior data engineers on data warehouse design, distributed systems thinking, and production data engineering practices.
Data Engineer, Insights Analytics Group
Mar 2020 – Jul 2022. 55-person analytics platform company providing data infrastructure to 180 SaaS and e-commerce clients
- Built 12 end-to-end ETL pipelines using Spark and Airflow, processing 120M+ daily events for customers ranging from 10M to 200M record datasets.
- Implemented streaming data ingestion architecture using Kafka, enabling real-time analytics dashboards serving 85K daily active users with sub-minute latency.
- Optimized S3-based data lake schema and partitioning strategy, reducing query cost by 52% and improving query performance by 2.8x for analytics team of 15.
Education
University of Colorado Boulder
Bachelor of Science, Computer Science. Sep 2016 – Dec 2019
Skills
Apache Spark, Airflow, Python, SQL, Kafka, Snowflake, ETL, Cloud Data Warehouses, Data Quality, Distributed Systems, Git, AWS
Languages
English, Polish
Why this look works
Data engineering demands precision and reliability. A structured, technical layout conveys infrastructure thinking. Cool blues and professional grays signal both technical depth and the reliability that data teams depend on.
Resume tips for this job
- Lead with data scale and pipeline reliability. 'Built ETL processing 450M+ daily records' and 'improved data availability from 94.2% to 99.8%' prove your capability at scale.
- Quantify performance improvements. Data engineers are hired to make pipelines faster, cheaper, and more reliable. Mention latency reductions, cost savings, and reliability gains you've achieved.
- Show both breadth and depth. Name your core technologies (Spark, Airflow, Snowflake) and mention depth: schema optimization, distributed systems thinking, or data quality frameworks you've built.
- Demonstrate systems thinking. Data engineering is more than code; mention how your pipelines enabled business outcomes: dashboards shipped, models trained, or analytics teams unblocked.
Common questions
Should I emphasize batch pipelines or streaming data infrastructure?
Show both if you have experience. Batch and streaming are complementary; modern data stacks need both. Lead with whichever is your stronger experience, but mention capability across both. Streaming is increasingly expected at scale.
I've worked primarily with one cloud platform like AWS. Does that limit my flexibility?
Data engineering principles transfer across clouds. Mention AWS expertise confidently and emphasize portable skills: Spark, Airflow, SQL, distributed systems thinking. Learning a new cloud platform is fast; data engineering fundamentals are permanent.
How do I show impact when my work is infrastructure that analysts use?
Quantify enablement: 'improved latency enabling 28 analytics teams to deploy dashboards faster' or 'reduced costs by 41% while improving performance by 3.2x'. Your infrastructure work directly enables or constrains what others can do; frame it that way.