Top Data Engineering Jobs in Charlotte, NC

Design, develop, test, and support scalable batch and real-time data pipelines. Partner across teams to define requirements, implement data governance, automate monitoring, troubleshoot performance, and mentor peers while productionalizing big data solutions on cloud platforms.

Lead design and build of a new graph-backed enterprise data platform: architect graph and relational storage, ingestion and ETL/ELT pipelines, entity resolution, temporal models, governance, APIs, and production deployments across cloud and customer-hosted environments.

As a Data Engineer at NinjaOne, you'll develop scalable data pipelines, manage relational databases, automate workflows, ensure data integrity, build dashboards, and collaborate with cross-functional teams to support data-driven decision-making.

The Senior Catastrophe Analyst will develop automated processes, design databases, create pricing tools, and provide analytics to drive catastrophe risk management.

Manage a team of data scientists to develop and validate statistical and machine learning models, leveraging large data sets to drive business decisions and ensure model effectiveness within the credit review context.

As a Senior Data Engineer at Gopuff, you'll design and scale data systems, build reliable pipelines, and ensure data quality. Collaborate with various teams to drive analytics and business impact.

As a Senior Data Engineer, you'll design and build scalable data pipelines and maintain a robust data platform while partnering with cross-functional teams to ensure high-quality data solutions that drive business impact.

As a Senior Data Engineer at Gopuff, you will design and scale data systems, build reliable data pipelines, and collaborate with cross-functional teams to enhance data quality and drive business impact.

As a Senior Data Engineer, you'll design and maintain data pipelines, enhance the Data Platform, and promote data quality across teams.

The Data Engineer will design, build, and maintain scalable data pipelines and collaborate cross-functionally to ensure high-quality data solutions. Responsibilities include architecture contribution, data quality assurance, and driving improvements in workflows and tooling.