Top Tech Jobs & Startup Jobs in Charlotte, NC

Lead migration of an on-premises Oracle PL/SQL system to Snowflake on AWS, design data models and ETL processes, write complex SQL transformations, coordinate cross-functional teams, support legacy and new applications, document procedures, and mentor other data engineers.

Build and maintain scalable cloud-native ETL/ELT pipelines and data platforms to support analytics and ML. Implement IaC and CI/CD, enable MLOps, ensure data quality, monitoring, and collaborate with data scientists and product teams to deploy production AI services.

Place and retain small commercial insurance accounts through wholesale brokers, using product and market expertise. Review complex submissions, advise agents, secure pricing, drive sales and retention, manage pipelines, resolve service issues, and collaborate with underwriting and internal partners. Hybrid role with territory goals and compliance responsibilities.

Design, build, and operate enterprise AI/ML platform capabilities (AIOps/MLOps/LLMOps). Develop GenAI features, experiment with models and services, and architect end-to-end solutions. Collaborate with data, cloud, and business teams, drive engineering execution, promote best practices, and mentor engineers. Ensure platform reliability, monitoring, and scalable deployments across cloud environments.

Design, build, and maintain scalable data assets and pipelines for analytics and ML; validate and ingest diverse data sources; perform exploratory data analysis and data quality checks; collaborate with cross-functional teams and produce reproducible code and documentation.

The Senior IT Engineer will lead full stack development projects focused on AWS, architecting APIs, mentoring junior engineers, and ensuring application performance and security.

Design and build production-scale data pipelines, orchestration, and integrations on GCP to enable metadata-driven governance, lineage, automation, and AI-enabled workflows. Implement secure, observable, cost-efficient cloud data solutions, embed governance into engineering workflows, and drive CI/CD, testing, monitoring, and platform engineering best practices.

Lead enterprise data platform engineering strategy and teams to build, modernize, and operate scalable, secure data platforms, analytics, AI-ready capabilities, and Third-Party Data enablement. Drive platform architecture, cloud modernization, integration, governance, observability, and operational excellence while partnering with stakeholders to deliver analytics, BI, and AI-enabling solutions and mentor engineering leaders across a global, matrixed organization.

Lead alignment of Enterprise Data Services or Applied AI & Analytics portfolios with enterprise data/AI strategy. Oversee portfolio prioritization, IT budgeting, financial accountability, vendor management, and value-stream coordination. Partner with executive stakeholders to meet efficiency targets and manage annual portfolio spend (~$90M+). Promote Agile/DevOps/Scaled Agile practices and continuous improvement.

Design, build, and operationalize production-grade AI/Machine Learning solutions and MLOps services. Deploy models and API services to AWS and GCP using CI/CD and IaC, implement workflow automation, monitor post-production performance, research generative/ML techniques, and mentor junior engineers while collaborating with cross-functional data, cloud, and architecture teams.