AWS Data Engineer

Propertyvalue Prudent Technologies And Consulting

Seattle, WA, United States

9 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

7 years minimum

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Amazon Web Services Data Analysis Databases Continuous Integration Data Architecture Information Engineering Data Integration Extract Transform Load (ETL) Data Migration Document-Oriented Databases Amazon DynamoDB Identity and Access Management

+27 more

Python (Programming Language) Microsoft SQL Server MongoDB NoSQL NumPy Cloud Services Data Streaming Data Processing Data Ingestion Sql Optimization Spring Cloud System Availability Change Data Capture Pandas Event Driven Architecture Database Migration Data Lakes Pyspark Cassandra AWS Glue Real Time Data Apache Kafka Data Management Cloudwatch Amazon Simple Queue Service (SQS) Stream Processing Data Pipelines

Job description

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, * 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.

Requirements

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.