Data Engineer - AWS/Oracle

We are seeking a hands-on Data Engineer Contractor to support our Reporting Instance (RI) and Self-Managed Hosting (SMH) project initiatives. The engineer will be responsible for developing and supporting scalable data pipelines, ETL processes, data validation, batch processing, and AWS-based data solutions. The ideal candidate has strong experience with AWS Glue, AWS Lambda, Oracle SQL/PL/SQL, and troubleshooting complex data environments.

• Design, develop, and support ETL/ELT data pipelines using AWS Glue.
• Develop and maintain AWS Lambda functions for data processing, automation, and event-driven workflows.
• Build and support data flows for downstream reporting and analytics systems.
• Work with Oracle SQL and PL/SQL to develop, troubleshoot, and optimize data processing workloads.
• Validate end-to-end data flows and ensure data quality, integrity, and consistency.
• Monitor and troubleshoot batch jobs, pipeline failures, data issues, and operational problems.
• Support AWS environment configuration, deployment, and operational readiness in collaboration with Infrastructure teams.
• Perform SQL and pipeline performance tuning, including query optimization, execution-plan analysis, indexing, materialized views, partitioning, and parallel processing where appropriate.
• Optimize ETL processes, batch jobs, and data refresh cycles for reliability and scalability.
• Create technical documentation and participate in knowledge transfer.

• Hands-on experience with AWS Glue and AWS Lambda.
• Strong experience developing and supporting data pipelines and ETL processes.
• Strong Oracle SQL and PL/SQL experience.
• Experience working with large-scale or high-volume data environments.
• Experience with data validation, troubleshooting, and batch processing.
• Understanding of AWS cloud services and data integration patterns.
• Strong problem-solving and communication skills.
• Ability to work independently in a fast-paced operational environment.

• Experience with Oracle performance tuning and execution-plan analysis.
• Experience with materialized views, indexing, partitioning, and parallel processing.
• Familiarity with batch scheduling and orchestration tools.
• Experience working with Infrastructure and cross-functional teams.