Top Tech Jobs & Startup Jobs in Charlotte, NC

Design, build, and operate scalable data transformation pipelines and dimensional models using dbt, SQL, and AWS. Implement infrastructure-as-code (AWS CDK), CI/CD, and query performance optimizations while ensuring data quality, governance, and collaboration with cross-functional and offshore teams.

Lead EXL's sports industry go-to-market and new business efforts. Develop strategy, co-create integrated digital and analytics solutions, build C-level relationships, win MSAs/SOWs, represent the firm publicly, and grow the sports portfolio across adjacent sectors.

Hands-on engineering role to design, build, and maintain scalable ETL/ELT data pipelines, data warehouses and lakes, and data models. Optimize batch and near-real-time processing, implement query optimization and data quality checks, support AI data workflows, and collaborate with architects, analysts, and stakeholders.

Senior AWS Analytics Engineer. Posting contains no detailed responsibilities or qualifications beyond the job title.

Design and deploy enterprise-grade generative AI systems across the 7-layer stack, select and fine-tune models, build LLMOps pipelines and observability, integrate with cloud platforms and APIs, enforce data protection, manage hallucinations, lead technical strategy, and act as onshore client technical liaison.

Senior Business Analyst for insurance data and systems: elicit and document business/functional requirements, create BRDs/FRDs and user stories, perform SQL-based data analysis and source-to-target mappings, support testing (SIT/UAT), collaborate with stakeholders, data engineers, QA and delivery teams across end-to-end Agile project lifecycle.

Lead data engineering teams to design, build, and maintain scalable cloud-native data pipelines, data platforms, and data products using Snowflake, Databricks, Airflow, and modern architectures. Optimize ETL/ELT, SQL, and transformations for analytics, reporting, and AI use cases; ensure data quality, governance, observability, and stakeholder alignment. Produce documentation, runbooks, and present technical solutions to business and technical audiences.

Design and deploy end-to-end Generative AI systems across the 7-layer stack. Select and fine-tune models, build LLMOps pipelines, implement observability, integrate with cloud platforms and APIs, enforce data protection, and lead stakeholder workshops to translate business needs into scalable, secure AI solutions.

Design and deploy enterprise Generative AI systems across the 7-layer stack: data ingestion, preprocessing, model selection, orchestration, inference, integration, and application. Lead model selection/tuning (commercial and open-source), establish LLMOps and observability, integrate AI with cloud platforms, enforce data protection, manage hallucinations/security, and act as onshore technical lead for client workshops and stakeholder alignment.

Lead analytics and AI initiatives: define strategy, translate business needs into AI/use cases, oversee dashboards and advanced analytics, run ML/LLM pilots, operationalize models, ensure responsible AI governance, and manage and mentor analytics teams while driving stakeholder alignment.