Top Tech Jobs & Startup Jobs in Charlotte, NC

Builds and maintains cloud-based data pipelines, transformations, semantic views, conformed dimensions, and analytical data models supporting reporting and AI/ML. Implements data quality checks, documentation, lineage, testing, release-readiness evidence, and defect resolution. Collaborates with data owners, governance, engineering, security, product, and customer teams to deliver reliable, governed data products. The role supports enterprise data modernization, migrations, DataOps, and production data platforms for a federal client.

Provides help desk and customer support for a federal cloud-based data and analytics platform. Responsibilities include triaging incidents and service requests, maintaining support records and customer histories, coordinating escalations across technical teams, analyzing recurring issues and support trends, documenting solutions, and tracking satisfaction, resolution, adoption, and service-level metrics.

Configure and improve CRM integrations, intake workflows, routing logic, automation, dashboards, and customer lifecycle processes. Translate business and service requirements into secure, auditable workflow capabilities, integrating CRM, ticketing, identity, provisioning, and support platforms. Collaborate with engineering, security, QA, product, governance, and customer-facing teams. Document requirements, process maps, decisions, risks, operating procedures, and delivery evidence while supporting continuous improvement and operational reporting.

Own contractor-side backlog readiness for an AI-focused delivery domain. Translate approved business needs into features, user stories, requirements, and acceptance criteria; lead discovery, stakeholder workshops, backlog refinement, dependency management, decision tracking, and sprint preparation. Collaborate with product, engineering, data science, security, governance, quality, and customer teams while documenting risks, decisions, and delivery evidence. Support senior product leadership and the government product owner in an Agile federal environment.

Designs and maintains production MLOps and LLMOps pipelines across Azure and Databricks environments. Responsibilities include model training, deployment, monitoring, retraining, versioning, CI/CD automation, infrastructure provisioning, model serving, testing, governance, and responsible AI controls. The role partners with data scientists, engineers, security, and governance teams to operationalize reliable, auditable machine learning solutions and troubleshoot production issues.

Develop and operationalize AI, machine learning, NLP, predictive analytics, and generative AI solutions for a federal enterprise data platform. Build reusable AI/ML patterns, agentic workflows, prompt orchestration, MLOps pipelines, monitoring, evaluation, guardrails, and human-in-the-loop processes using Azure, Databricks, MLflow, and Python. Collaborate with engineering, security, governance, and business stakeholders to deliver scalable, trustworthy, governed, and production-ready AI capabilities.

Builds and maintains cloud data pipelines, transformations, semantic views, conformed dimensions, and analytical data models. Implements data quality controls, lineage, documentation, testing, release-readiness evidence, and defect resolution. Collaborates with data owners, governance teams, engineers, security stakeholders, data scientists, and customers to deliver reliable, governed data products supporting analytics, reporting, self-service BI, and AI/ML use cases.

Leads product operations and integrated planning for a large federal data and analytics modernization platform. Maintains roadmaps, backlogs, delivery plans, dependency logs, decision tracking, release alignment, dashboards, and Agile metrics. Coordinates Product Managers, engineering, security, governance, quality, and delivery stakeholders while managing risks, dependencies, and escalations. Supports PI planning, release planning, cross-team execution, delivery reporting, traceability, and continuous improvement across complex technology programs.

Supports data stewardship and governance workflows, including metadata, taxonomies, data-use agreements, MOUs, ISAs, responsible data-use practices, and governance evidence. Provides Trustworthy AI governance support through model documentation, risk assessments, monitoring outputs, issue logs, and corrective-action tracking. Collaborates with technical, security, quality, governance, and customer-facing stakeholders while documenting decisions, risks, and delivery evidence.

Defines and monitors data-quality rules, dimensions, freshness SLAs, publication readiness, and governance metrics. Coordinates with data owners, stewards, engineers, and governance stakeholders to establish measurable standards and remediation plans. Supports DataGov reporting, trend analysis, corrective-action tracking, data profiling, quality assessments, dashboards, and executive reporting. Documents findings, risks, decisions, and delivery evidence while supporting data privacy, compliance, and continuous improvement in a federal data modernization environment.