Professional Resume Template for

Data Engineer

Aria M. Thorne

Seattle, WA

(206) 555-0143

aria.thorne@email.com

linkedin.com/in/aria-thorne | github.com/ariathorne | ariathorne.dev

Professional Summary

Systems-oriented Data Engineer with 6 years of career focus on architecting distributed lakehouse architectures and real-time streaming pipelines. Expertise includes designing ETL/ELT workflows handling 100M+ daily events, optimizing Spark query execution times, and managing cloud storage infrastructure on AWS and GCP. Successfully reduced API data latency by 45% and cut monthly database storage costs by 28% through custom partitioning and schema optimization. Proficient in Python, SQL, Apache Spark, Kafka, Airflow, dbt, Snowflake, and Databricks.

Work Experience

Data Engineer II — Sentry Analytics

Seattle, WA | July 2022 – Present

  • Designed and implemented a real-time data streaming pipeline using Apache Kafka and Spark Streaming, handling ingestion of 120M events per day with a 99.99% pipeline uptime.
  • Migrated legacy on-premise warehouse to AWS Snowflake, reducing query response times by 52% and annual licensing costs by $140,000.
  • Orchestrated 40+ complex data pipelines using Apache Airflow, improving workflow reliability from 88% to 99.6% through automated retries and alerting systems.
  • Optimized PySpark batch processing jobs, cutting resource consumption on AWS EMR by 35% and saving 12 engineering hours per week in pipeline maintenance.

Data Engineer — Clarity Retail Systems

Bellevue, WA | June 2020 – June 2022

  • Developed ETL pipelines using Python and SQL to ingest sales transaction data from 400+ retail stores, processing $12M in daily sales volumes.
  • Refactored relational database schemas and indexed PostgreSQL tables, improving reporting dashboard refresh speeds by 40%.
  • Implemented data quality validation checks using Great Expectations, reducing data errors in executive reports by 65%.
  • Built data extraction scripts from 5 third-party APIs, expanding the customer profile database by 1.8M verified records.

Education

Bachelor of Science in Computer Science

University of Washington · Seattle, WA · 2020

Skills

Python, SQL, Apache Spark, Apache Kafka, Apache Airflow, dbt, Databricks, Snowflake, PostgreSQL, AWS (S3, EMR, Redshift, Glue), GCP (BigQuery, Cloud Composer), Terraform, Delta Lake, Great Expectations, CI/CD

Projects

Enterprise Lakehouse Migration

Role: Lead Data Engineer

Tools: Databricks, Delta Lake, AWS, Apache Airflow

Architected a migration from Redshift to Databricks Lakehouse, processing 800M records and reducing query latency by 45%.

Real-time Ingestion Architecture

Role: Data Engineer

Tools: Apache Kafka, PySpark, AWS Kinesis, Terraform

Built a streaming data ingestion pipeline for credit transaction verification, processing 4,000 events per second with sub-second latency.

Certifications

  • AWS Certified Data Engineer – Associate (2024)
  • Google Cloud Professional Data Engineer (2023)

Additional information

  • Languages: English (Native), French (Conversational)
  • Volunteer Work: Technical mentor for regional Girls Who Code chapter (2022-present)
  • Availability: 2 weeks notice

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Job Market Insights

Market data and opportunities for

Data Engineer

Job Market Insights

$115,000

-

$150,000

Avg:

$132,000

Growth Outlook:

The demand for skilled Data Engineers in the United States remains extremely high, driven by the expansion of cloud data platforms and the rapid integration of artificial intelligence and machine learning workloads. As organizations focus on optimizing data infrastructure costs and migrating to modern lakehouse architectures, Data Engineers who can build scalable, secure pipelines are in short supply. Employment in related data and software engineering fields is projected to grow by 25% over the next decade, with the global data engineering services market growing at a 15% CAGR.

15% CAGR through 2031

Key Skills Required

Focus on these skills when customizing your resume for recruiter screenings.

Proficiency in SQL, Python, and cloud-native data architectures (AWS, GCP, or Azure) || Hands-on experience building ETL/ELT pipelines using frameworks like Apache Spark, Airflow, and dbt || Strong understanding of data modeling, data warehousing, and distributed query performance optimization

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FAQ

Common questions about the

Data Engineer

position

What is the typical career path for a Data Engineer?
Which certifications are most highly valued for Data Engineers?
What is the difference between ETL and ELT architectures?
Why is Apache Spark preferred for big data processing?
How does dbt (data build tool) fit into the modern data stack?
What is a lakehouse architecture?
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