Data & Analytics

Data Engineer Resume Example & ATS Keywords (2026)

Professional Summary Example

“Data engineer with 5+ years of experience building and scaling data infrastructure for analytics and machine learning teams at high-growth technology companies. Expert in dbt, Airflow, and Snowflake with a track record of reducing data pipeline failures by 90%, cutting data warehouse costs by $320K annually, and enabling analytics for 200+ internal users through well-documented, reliable data products. Passionate about data reliability, observability, and treating data infrastructure as a product.”

Key Skills

PythonSQLdbtApache AirflowApache SparkKafkaSnowflakeBigQueryDatabricksAWS (S3, Glue, Lambda)TerraformGit

Experience Bullet Points

Use these achievement-oriented bullet points as inspiration for your own data engineer resume:

  • Built and maintained 85+ production data pipelines using Apache Airflow and dbt, processing 50M+ daily events with 99.7% pipeline reliability across 12-month period
  • Designed and implemented core data warehouse on Snowflake, establishing dimensional models and dbt transformation layer used by 200+ analysts and data scientists daily
  • Reduced data warehouse compute costs by $320K annually through query optimization, clustering key configuration, and aggressive use of Snowflake result caching
  • Built real-time streaming pipeline on Apache Kafka processing 8M events per hour, enabling real-time dashboard refresh for operations team and reducing insight lag from 24 hours to <5 minutes
  • Implemented data quality framework with Great Expectations, catching 95% of data anomalies before they reached downstream analytics and eliminating trust issues that previously required 30% analyst time
  • Led migration from legacy Hadoop data lake to modern lakehouse architecture (Databricks Delta Lake), reducing average query time from 45 minutes to 4 minutes for complex analytical queries
  • Developed self-serve data catalog with Datahub, documenting 400+ datasets and enabling analysts to find and understand data independently, reducing data team ad hoc requests by 40%
  • Mentored 3 junior data engineers through structured code reviews, dbt best practices documentation, and architecture design sessions

Education & Certifications

Education

Bachelor's in Computer Science or Statistics

Certifications

  • ✓Databricks Certified Data Engineer Associate
  • ✓Snowflake SnowPro Core
  • ✓dbt Certified Developer
  • ✓AWS Certified Data Analytics Specialty

Top ATS Keywords for Data Engineer Resumes

Include these keywords naturally throughout your resume to pass Applicant Tracking System filters:

data engineeringETL/ELTdata pipelinesApache SparkdbtAirflowSnowflakeBigQuerydata warehousestreaming data

Data Engineer Resume Tips

  1. 1

    Pipeline reliability metrics (99.7% uptime) and data volume processed (50M daily events) are the clearest data engineering scale signals.

  2. 2

    dbt has become the most searched data transformation tool — list it prominently if you use it.

  3. 3

    Cost optimization experience (cloud data warehouse spend reduction) is a significant differentiator — include dollar amounts if possible.

  4. 4

    Data quality framework experience is increasingly valued — mention the tool (Great Expectations, dbt tests, Monte Carlo) and the outcome.

Frequently Asked Questions

What is the most in-demand data engineering stack in 2026?+

The modern data stack most commonly searched: dbt (transformation), Airflow or Prefect (orchestration), Snowflake or BigQuery (data warehouse), and Python (scripting). Spark and Kafka are essential for large-scale or streaming contexts. Databricks is growing rapidly, particularly for ML-heavy data platforms.

What is the difference between a data engineer and a software engineer?+

Data engineers specialize in data infrastructure: pipelines, data warehouses, transformation logic, and data quality. Software engineers build application features and user-facing systems. Data engineers need strong SQL, Python, and distributed systems knowledge rather than application frameworks. The roles overlap in infrastructure engineering and platform engineering.

Should data engineers know machine learning?+

Awareness and integration skills are important — building feature pipelines for ML models, serving ML features at low latency, and MLOps infrastructure. Full ML modeling skills are not required for most data engineering roles. The distinction between data engineer and ML engineer is narrowing at some companies.

Further Reading

How to Beat ATS Systems

10 proven strategies to optimize your resume for ATS filters.

Resume Keywords Guide

50+ keywords that get you hired, by industry and role.

AI Resume Optimization

How AI can 3x your interview rate while maintaining authenticity.

Optimize your data engineer resume

Get your free ATS score and AI-powered optimization suggestions tailored for data engineer roles.

Try MatchMyResumes Free

Resume Format

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Data Engineer

Professional Resume

Professional Summary

Data engineer with 5+ years of experience building and scaling data infrastructure for analytics and machine learning teams at high-growth technology companies. Expert in dbt, Airflow, and Snowflake with a track record of reducing data pipeline failures by 90%, cutting data warehouse costs by $320K annually, and enabling analytics for 200+ internal users through well-documented, reliable data products. Passionate about data reliability, observability, and treating data infrastructure as a product.

Experience

  • Built and maintained 85+ production data pipelines using Apache Airflow and dbt, processing 50M+ daily events with 99.7% pipeline reliability across 12-month period
  • Designed and implemented core data warehouse on Snowflake, establishing dimensional models and dbt transformation layer used by 200+ analysts and data scientists daily
  • Reduced data warehouse compute costs by $320K annually through query optimization, clustering key configuration, and aggressive use of Snowflake result caching
  • Built real-time streaming pipeline on Apache Kafka processing 8M events per hour, enabling real-time dashboard refresh for operations team and reducing insight lag from 24 hours to <5 minutes
  • Implemented data quality framework with Great Expectations, catching 95% of data anomalies before they reached downstream analytics and eliminating trust issues that previously required 30% analyst time
  • Led migration from legacy Hadoop data lake to modern lakehouse architecture (Databricks Delta Lake), reducing average query time from 45 minutes to 4 minutes for complex analytical queries
  • Developed self-serve data catalog with Datahub, documenting 400+ datasets and enabling analysts to find and understand data independently, reducing data team ad hoc requests by 40%
  • Mentored 3 junior data engineers through structured code reviews, dbt best practices documentation, and architecture design sessions

Skills

Python, SQL, dbt, Apache Airflow, Apache Spark, Kafka, Snowflake, BigQuery, Databricks, AWS (S3, Glue, Lambda), Terraform, Git

Education & Certifications

Bachelor's in Computer Science or Statistics

  • Databricks Certified Data Engineer Associate
  • Snowflake SnowPro Core
  • dbt Certified Developer
  • AWS Certified Data Analytics Specialty

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