Professional Resume Template for
AWS Data Engineer
Nolan S. Carter
Seattle, WA
(206) 555-0187
nolan.carter@email.com
linkedin.com/in/nolan-carter | github.com/nolancarter | nolancarter.dev
Professional Summary
Systems-oriented AWS Data Engineer with 6 years of backend engineering tenure designing, optimizing, and scaling high-throughput data pipelines in cloud environments. Expertise includes configuring serverless ETL pipelines, orchestrating workflow schedules, and partitioning cloud data warehouses. Successfully reduced data latency by 43% and cut AWS infrastructure costs by 28% through EMR cluster auto-scaling and Glue schema evolution. Proficient in Python, SQL, PySpark, AWS Glue, EMR, Redshift, Athena, and Terraform.
Work Experience
AWS Data Engineer — Clarity Analytics
Seattle, WA | January 2024 – Present
- Architected serverless ETL pipelines using AWS Glue, Lambda, and Step Functions to process 4.2TB of daily transaction data, reducing pipeline execution runtime by 34%.
- Optimized Amazon Redshift warehouse queries and distribution keys, yielding a 48% speedup in business intelligence reports for a user base of 120 global analytics stakeholders.
- Implemented Apache Spark (PySpark) applications running on Amazon EMR to clean unstructured data, cutting data ingestion error rates from 4.8% to under 0.6% over a 6-month period.
- Provisioned secure multi-account data lake environments using Terraform and AWS Lake Formation, reducing data access provisioning latency by 18 hours per data request.
Data Engineer — Aether FinTech
Chicago, IL | June 2020 – December 2023
- Constructed real-time streaming data ingestion pipelines utilizing AWS Kinesis and Lambda to process 2,500 messages per second, lowering overall system telemetry latency by 32%.
- Migrated legacy on-premise ETL workflows to Amazon Web Services using AWS Database Migration Service (DMS), transitioning 14 databases with zero operational disruption.
- Automated pipeline DAGs via Apache Airflow (Amazon MWAA), coordinating 60+ interdependent tasks and saving the engineering team 12 hours of manual monitoring weekly.
- Implemented Amazon S3 intelligent tiering and lifecycle policies for 280TB of historical logs, reducing monthly cloud storage expenditures by 26% ($18,400 monthly savings).
Education
Bachelor of Science in Computer Science
Purdue University · West Lafayette, IN · 2020
Skills
Python, SQL, PySpark, AWS Glue, Amazon EMR, Amazon Redshift, Amazon Athena, Amazon S3, AWS Lambda, AWS Step Functions, Amazon Kinesis, AWS Lake Formation, Terraform, Docker, Apache Airflow, Git, Apache Kafka, Scala, Relational Databases, NoSQL
Projects
Real-Time Fraud Data Ingestion
Role: Lead Data Engineer
Tools: AWS Kinesis, AWS Lambda, DynamoDB, Terraform
Designed a serverless data ingestion pipeline to capture and process credit card transactions, analyzing 3,500 requests/sec with average pipeline processing latency under 85ms.
Cloud Data Lake Migration
Role: AWS Data Engineer
Tools: AWS Glue, Apache Airflow, AWS Lake Formation, Amazon S3
Architected a secure data lake repository for 650TB of structured and unstructured logs, reducing pipeline failures by 38% and saving $42,000 in annual licensing costs.
Certifications
- AWS Certified Data Engineer – Associate (2024)
- AWS Certified Solutions Architect – Associate (2022)
Additional information
- Languages: English (Native), Spanish (Conversational)
- Volunteer Work: Technical mentor for Purdue undergraduate coding groups (2021-present)
- Availability: 2 weeks notice
Job Market Insights
Market data and opportunities for
AWS Data Engineer
Job Market Insights
$119,000
-
$150,000
Avg:
$134,000
Growth Outlook:
The demand for database administrators, architects, and data engineers is projected to grow by 15% from 2024 to 2034, much faster than the average for all occupations. The massive expansion of cloud migration, big data integration, and real-time artificial intelligence systems drives a strong need for data professionals. AWS data infrastructures are critical for enterprise-wide analytics platforms, enabling businesses to leverage streaming analytics and scalable machine learning pipelines safely.
15% growth over 10 years
Key Skills Required
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