We are looking for a Data Engineer to support and enhance critical data operations in Greenville, South Carolina. This role focuses on keeping data platforms dependable, efficient, and scalable across both real-time and scheduled workflows. The ideal candidate will bring strong technical expertise in cloud-based data environments and a proactive approach to improving performance, automation, and data reliability.<br><br>Responsibilities:<br>• Oversee the health and performance of data pipelines that run across Snowflake, Kafka, and connected platforms.<br>• Investigate operational issues affecting data ingestion, transformation, or downstream delivery and drive timely resolution.<br>• Maintain stable batch and streaming processes by improving resiliency, uptime, and overall execution efficiency.<br>• Administer Snowflake resources, including warehouses, databases, permissions, and usage optimization.<br>• Manage Kafka infrastructure by tuning clusters, topics, partitions, and consumer group behavior for reliable throughput.<br>• Create and maintain automated solutions for deployment, monitoring, failure recovery, and routine workflow support.<br>• Develop operational scripts and utilities using Python, Bash, and related tools to reduce manual effort and improve consistency.<br>• Contribute to CI/CD practices that strengthen the release and maintenance process for data infrastructure.<br>• Partner with engineering and analytics teams to improve pipeline design, data performance, and delivery accuracy.<br>• Support data governance, security, compliance, and data quality standards through validation checks and alerting frameworks.
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.
We are looking for a Data Engineer to join a financial services organization in Greer, South Carolina on a contract basis with the potential for a permanent role. This role focuses on designing and delivering modern data pipelines in a cloud-based environment, with an emphasis on reliability, quality, and scalable data processing. The position offers the opportunity to contribute to both new development and targeted improvements across an evolving data ecosystem centered on Snowflake and event-driven ingestion.<br><br>Responsibilities:<br>• Design, build, and deliver end-to-end data pipelines in Snowflake to support business and analytics needs.<br>• Create new data integration workflows while troubleshooting and resolving issues in existing pipelines.<br>• Apply sound engineering practices for coding, documentation, testing, and deployment to improve consistency and maintainability.<br>• Balance hands-on development of new solutions with optimization work that improves performance, stability, and efficiency.<br>• Develop streaming and ingestion processes using Kafka to enable timely and dependable data movement.<br>• Strengthen observability and data quality controls so pipeline health and accuracy are easier to monitor and maintain.<br>• Help reduce technical debt by simplifying legacy data processes and modernizing pipeline design where appropriate.<br>• Contribute to AI-assisted engineering efforts by using approved tools to accelerate development, testing, and documentation activities.