We are looking for a Data Engineer to join a financial services organization in Greer, South Carolina on a contract-to-permanent basis. This role focuses on building and enhancing modern data pipelines within a cloud-centered environment, with Snowflake serving as the primary data platform. The ideal candidate will help deliver production-ready solutions, strengthen data reliability, and apply disciplined engineering practices to support scalable, near real-time data processing.<br><br>Responsibilities:<br>• Design, build, and deliver end-to-end data pipelines in Snowflake that support business reporting and data consumption needs.<br>• Create new ingestion and transformation workflows while troubleshooting pipeline issues to improve stability and performance.<br>• Contribute to a delivery model that balances new development with targeted optimization of existing data assets and workflows.<br>• Implement streaming and event-driven ingestion patterns using Kafka to support timely and scalable data movement.<br>• Improve observability across the data ecosystem by strengthening monitoring, alerting, and data quality controls.<br>• Help simplify legacy data processes by reducing technical debt and modernizing outdated pipeline components.<br>• Apply sound software engineering standards, including maintainable code, documentation, and repeatable development practices.<br>• Support the advancement of testing and CI/CD processes by helping establish more consistent engineering workflows.<br>• Leverage AI-assisted development tools to accelerate coding, validation, and technical documentation where appropriate.

• Hands-on experience building and maintaining data pipelines using Snowflake.<br>• Practical knowledge of Apache Kafka for streaming or event-based data ingestion.<br>• Experience working in cloud-based data environments with a focus on scalable architecture.<br>• Familiarity with Azure Databricks and its role within broader data engineering ecosystems.<br>• Understanding of data quality, monitoring, and observability practices in production environments.<br>• Ability to troubleshoot pipeline failures and resolve performance or reliability issues efficiently.<br>• Experience following engineering best practices for version control, testing, and deployment.<br>• Exposure to AI-assisted development tools such as Claude or similar coding support platforms.

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