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AWS Data Engineer

Prudent Technologies and ConsultingSeattle, WA🇺🇸United StatesPosted 24 Aug 2026

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
Seattle, WA, United States
Posted
Yesterday
DynamoDBMongoDBSQLSQL ServerAWSETLNumPyCassandraKafkaPandasPythonStakeholder Management

Job Description

Required Skills
✅ AWS Glue
✅ AWS DMS (Database Migration Service)
✅ CDC (Change Data Capture)
✅ Python / PySpark
✅ Kafka or SNS/SQS
✅ Data Lake Architecture
✅ SQL & Database Design
✅ AWS CloudWatch
✅ AWS CloudTrail
✅ Data Integration & ETL Development
Preferred Skills

  • AWS IAM
  • Amazon EKS
  • EventBridge
  • MongoDB, Cassandra, DynamoDB
  • Data Migration & Modernization Projects
  • Real-Time Streaming Architectures
  • CI/CD for Data Platforms

Key Responsibilities

  • Design, develop, and maintain scalable data pipelines in AWS using AWS Glue, Python, and PySpark.
  • Build robust data ingestion frameworks to collect and process data from multiple on-premises and cloud-based sources.
  • Implement Change Data Capture (CDC) solutions for near real-time data synchronization and replication.
  • Utilize AWS Database Migration Service (AWS DMS) to migrate and replicate data across enterprise systems with minimal downtime.
  • Design and implement event-driven architectures using Kafka, Amazon SNS, Amazon SQS, and EventBridge.
  • Develop cloud-native data processing solutions supporting high availability, scalability, and performance.
  • Build, optimize, and maintain enterprise Data Lake solutions.
  • Monitor and troubleshoot data workflows using AWS CloudWatch and AWS CloudTrail.
  • Develop efficient Python-based data processing applications leveraging libraries such as Pandas and NumPy.
  • Work with relational and NoSQL databases including SQL Server, DynamoDB, MongoDB, and Cassandra.
  • Support migration and modernization of legacy data pipelines into AWS-based architectures.
  • Collaborate with business stakeholders, application owners, and data teams to understand data architecture, business requirements, and analytics needs.
  • Document data models, ETL processes, data flows, and target-state architectures.
  • Drive continuous improvements in data quality, platform performance, security, and operational efficiency.

Qualifications

  • 7+ years of experience in Data Engineering and Data Integration.
  • Hands-on experience with AWS-based data platforms and cloud-native applications.
  • Strong expertise in Python and PySpark development.
  • Experience implementing CDC and real-time data streaming solutions.
  • Strong understanding of data modeling, SQL optimization, and enterprise data architecture.
  • Excellent communication and stakeholder management skills.

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