Top Data & Analytics Jobs in Raleigh, NC

Leverage data analytics, business intelligence, and Python to deliver strategic insights and tailored solutions for clients. Build reports, dashboards, data pipelines, and support data architecture and quality initiatives. Analyze data from multiple sources to identify trends, improve operations, and inform decision-making. Collaborate with stakeholders and team members while continuously developing technical skills and contributing to process improvements.

Leverage data analytics, business intelligence, and data science techniques to deliver strategic insights and solutions for clients. Build reports, dashboards, data pipelines, visualizations, and data architecture using tools such as Oracle BI, QlikView, Spotfire, and BIRT. Ensure data quality, manage stakeholder relationships, improve analytics processes, interpret complex datasets, and mentor junior team members while contributing to client projects and business strategy.

Build and maintain data pipelines integrating laboratory instruments, automation systems, operational platforms, APIs, robotics, databases, and files. Develop SQL transformations, data models, validation checks, orchestration, monitoring, and alerting to support analytics, dashboards, troubleshooting, and AI systems. Document infrastructure and datasets for regulatory compliance while collaborating with engineering, laboratory operations, data science, and automation teams. This is an on-site role with on-call and occasional weekend or holiday support.

Provide technical leadership for analytics engineering across enterprise platforms. Design and optimize scalable Databricks data pipelines using Python, PySpark, and SQL; develop governed Power BI solutions, semantic models, dashboards, and executive reporting. Partner with stakeholders to define metrics, deliver AI-enabled insights, and improve decision-making. Ensure data quality, lineage, security, and governance while optimizing models, queries, and pipelines. Lead large-scale technical initiatives, influence architecture, and guide analytics engineers.

The Senior Data Engineer will transform real-world data into analytics-ready datasets, build scalable pipelines, and conduct analyses to support scientific and business decisions, collaborating closely with data science teams.

Monitor and verify real-time college and professional sports data feeds, identify and resolve discrepancies, cross-check information across sources, troubleshoot issues with technical teams, track sports schedules and statistics, and provide feedback to data collection scouts to improve accuracy and performance.

Build and maintain AI billing, metering, consumption, and allocation data models. Reconcile usage and billing data across vendors, ensure data quality, and develop dashboards showing costs, utilization, trends, variances, and budget performance. Investigate anomalies and optimization opportunities while partnering with technology, finance, and business stakeholders on forecasting, governance, chargeback, cost transparency, and AI investment decisions.

Lead the design and implementation of enterprise-scale Knowledge Graphs and Semantic AI solutions, integrating graph intelligence and Generative AI. Manage cross-functional teams in data architecture and governance.

Architects FHIR-compliant clinical data models and develops harmonization, enrichment, and validation strategies across EHR, HIE, and claims data. Maintains clinical ontologies and value sets, ensures data accuracy and longitudinal integrity, and documents technical requirements for engineering teams and stakeholders. The role requires advanced Python and SQL skills, healthcare interoperability expertise, and experience with clinical informatics or data integration.

Leads business intelligence and data strategy by integrating organizational systems, analyzing large datasets, and delivering actionable insights. Advises leadership and revenue-generating teams on attendance, visitor experience, fundraising, profitability, and operational improvements. Oversees data modeling, visualization, process design, project execution, budgeting, stakeholder collaboration, mentoring, and data-driven decision-making.

Analyze gaming performance across game types, venues, states, devices, days, and player demographics. Produce quarterly reports, identify gameplay and player-behavior trends, and recommend data-driven improvements to product strategy, engagement, and revenue. Partner with account teams, visualize findings in Tableau and Power BI, and present actionable insights to leadership and field teams. Manage multiple priorities while working with ambiguous, semi-structured data and AI-assisted analytical tools.