Top Analysis Reporting Jobs in Raleigh, NC

Senior Data Engineer responsible for Epic and EHR integrations supporting value-based care, risk and quality workflows, member attribution, roster management, and care-gap tools. The role designs and executes EHR development tasks, coordinates with clinical, data, operations, and development teams, documents business and data flows, evaluates AI and automation opportunities, and develops reporting to identify data-quality issues and prevent outages.

Leads customer insights and measurement initiatives by translating Voice of Customer, behavioral, operational, and business data into actionable recommendations. Defines cross-channel success metrics, governs measurement standards and instrumentation, develops automated dashboards and AI-powered insights, and drives closed-loop feedback processes. Partners with product, technology, operations, and business teams to influence priorities, improve digital experiences, and measure business impact.

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.

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.

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.

Own and maintain the commercial SQL Server database, data pipelines, integrations, master and reference data, validation processes, and external market-data ingestion. Build trusted datasets connecting sales, product, customer, territory, and market information to Power BI reporting. Troubleshoot production incidents, improve pipeline reliability and performance, document data lineage and definitions, and serve as the commercial analytics data expert while partnering with stakeholders and source-system owners.

Designs, builds, tests, and supports enterprise data pipelines and Snowflake archival solutions for acquisitions, integrations, reporting, compliance, and analytics. Translates business and technical requirements into mappings, transformations, quality rules, and deployment plans. Collaborates with architecture, security, infrastructure, application, business, and vendor teams. Oversees validation, monitoring, documentation, cutover, go-live readiness, hypercare, and operational handoff while improving reusable, secure, cost-efficient data engineering practices.

Analyze large and complex datasets to identify debit card fraud signals, detect anomalies and coordinated fraud attacks, and implement prevention strategies in real-time decisioning systems. Partner with data scientists, fraud strategists, and business stakeholders to refine rules and models, conduct fraud hunting and data discovery, communicate actionable insights, and influence fraud prevention product decisions.

Leads causal inference studies, clinical outcomes analysis, member insights, and strategic opportunity sizing. Translates ambiguous business questions into analytical approaches, applies statistics and machine learning, establishes analytics best practices, mentors analytics professionals, and presents recommendations to executive leadership. Requires healthcare payer data expertise, advanced quantitative methods, strong SQL and Python skills, and experience with cloud data platforms and scalable analytical datasets.