AWS Data Engineer

o S3
o EventBridge
o Kinesis (Data Streams & Firehose)
o Glue (ETL + Crawlers)
o Step Functions
o Amazon Connect
o Athena
o Macie
Proficient in:
o Python
o PySpark
• Experience in:
o Glue Crawlers for schema discovery and cataloging
o Parquet-based data storage
o Building scalable data pipelines
• Strong understanding of event-driven architectures (Pub/Sub model)
• Hands-on experience with Terraform (Infrastructure as Code)
• Familiarity with CI/CD tools and automation pipelines
o Messaging platforms like Kafka and Amazon EventBridge
o Designing real-time data processing systems
o Using Glue Crawlers + Athena for data lake architectures________________________________________
Roles & Responsibilities
 • Develop a comprehensive plan for migrating near real-time fraud detection campaigns from on-premises systems to AWS.
• Design and implement event-driven architectures to process inbound dialer data (fraud events) using services such as Amazon EventBridge, Kafka, Kinesis Data Streams, and Kinesis Firehose.
• Build and manage scalable data pipelines using AWS Glue (ETL jobs, Crawlers), PySpark, and Python for data ingestion, transformation, and processing.
• Configure and manage Glue Crawlers to automatically discover schemas and update the Data Catalog.
• Store and optimize data using Parquet format and enable analytics through Amazon Athena for efficient querying.
• Develop integrations between Customer Profiles and messaging platforms to automatically trigger profile updates and downstream processes.
• Implement automation to trigger fraud-related outbound calls based on updates in customer profiles.
• Design and orchestrate workflows using AWS Step Functions to manage complex processing pipelines.
• Provision and manage cloud infrastructure using Terraform (Infrastructure as Code).
• Optimize system architecture for scalability, reliability, cost-efficiency, and ensure data integrity and security.
• Conduct end-to-end testing o f the entire framework to validate functionality, performance, and reliability.
• Deploy, automate, and manage resources using CI/CD pipelines.
• Continuously monitor system performance and implement optimizations post-deployment.
• Maintain detailed documentation of architecture, workflows, and operational processes.