User Workload Engineer

We are seeking a skilled Data Engineer to lead the planning, execution, and validation of enterprise-level data migration strategies. You will be responsible for transitioning critical workloads from legacy environments to modern, scalable cloud-based architectures. This role focuses on integrating specialized financial modeling systems and graph data structures into a robust AWS ecosystem.

Our client is a leading global computer software organization known for delivering innovative enterprise solutions. They operate at a significant scale, supporting complex data ecosystems for a diverse range of international clients. The company fosters a collaborative, high-performance culture that prioritizes technological advancement and professional growth.

Responsibilities

• Refactor, deploy, and maintain robust Java APIs to ensure seamless connectivity between application layers and the AWS data ecosystem.
• Architect and build high-performance, parallel data streams utilizing native cloud services including AWS Glue, Amazon S3, and Amazon Athena.
• Develop automated validation scripts to audit rows, schemas, and relational integrity to ensure zero data loss during migration.
• Optimize data extraction processes to eliminate on-premise bottlenecks and manage API throttling within AWS.
• Collaborate with cross-functional teams to migrate complex user workloads and custom application interfaces.
• Utilize Python and PySpark to build scalable data processing pipelines.
• Provide technical expertise in troubleshooting migration issues and ensuring system performance post-deployment.

Requirements

• 7-12 years of professional experience in data engineering, with a strong focus on legacy-to-cloud migrations.
• Expertise in Java development, specifically in building, debugging, and migrating enterprise APIs and data connectors.
• Deep hands-on experience with the AWS data stack, including AWS Glue, Amazon S3, and Amazon Athena.
• Proven experience integrating SAS or SASFM environments into broader data pipelines.
• Proficiency in Python and PySpark for data manipulation and pipeline development.
• Strong understanding of data migration best practices and relational database integrity.
• AWS Certified Data Engineer or AWS Certified Solutions Architect certification is highly preferred.
• Familiarity with Hadoop and Neo4j is considered a significant advantage.