AWS Data Engineer (Glue / Redshift / Python)

Location: Seattle, WA (Hybrid – 3 Days Onsite)
Duration: Long-Term Contract (12+ Months)
Work Authorization: U.S. Citizen or Green Card Holder or EAD

About the Role

We are seeking an experienced AWS Data Engineer to help build and scale modern cloud-based data platforms that power business intelligence, analytics, and machine learning initiatives. This is an excellent opportunity to work on large-scale data processing solutions, optimize data pipelines, and contribute to a growing data ecosystem built on AWS.

You'll work alongside data architects, analysts, and data scientists to design reliable, high-performance data solutions that drive critical business decisions.

What You'll Be Doing

• Design, develop, and optimize scalable ETL/ELT pipelines using AWS-native services
• Build and maintain cloud-based data lake and data warehouse solutions supporting analytics and reporting workloads
• Develop robust data integration frameworks using Python and PySpark
• Design and optimize Amazon Redshift data models and query performance
• Implement data quality, monitoring, and observability solutions across the data platform
• Collaborate with data scientists to operationalize machine learning datasets and feature pipelines
• Improve data governance, security, and compliance practices across AWS environments
• Support automation initiatives and drive continuous improvements in data processing performance and reliability
• Manage software asset, entitlement, and licensing data workflows through ServiceNow integrations

What We're Looking For

• 5+ years of Data Engineering experience building enterprise-scale data solutions
• 3+ years of hands-on AWS experience in production environments
• Strong expertise in Python, SQL, and data modeling
• Experience with AWS Glue, Redshift, Lambda, S3, Step Functions, and related AWS services
• Strong understanding of ETL/ELT architecture, data integration patterns, and workflow orchestration
• Hands-on experience with Apache Spark and PySpark
• Experience optimizing large datasets for performance, scalability, and cost efficiency
• Knowledge of data governance, security controls, IAM, encryption, and data lifecycle management

Nice to Have

• AWS Certified Data Analytics – Specialty or related AWS certifications
• Experience supporting machine learning and advanced analytics workloads
• Familiarity with Airflow, Databricks, Snowflake, or modern data platform technologies
• Experience working in Agile development environments

Pay: $72.00 - $80.00 per hour

Work Location: In person