Top Analysis Reporting Jobs in Jacksonville, FL

Lead the organization’s analytics strategy, manage a team of analysts, conduct advanced data analysis, and deliver actionable insights to stakeholders. Oversee analytics tools, scalable data systems, and data governance practices while collaborating with marketing, sales, operations, and product teams. The role requires expertise in BI platforms, programming languages, big data technologies, cloud platforms, web analytics, and experimentation tools.

Leads WWT’s Data Reliability and AI Readiness pillar, establishing enterprise AI data strategy, governance, certification, lineage, quality standards, and readiness scoring. Coordinates cross-functional data initiatives, improves operational data, owns trusted data products and AI-powered reporting, and manages a product owner and engineering team. Partners with IT, AI engineering, analytics, and business teams to deliver reliable, reusable, production-ready data while improving data economics and translating internal capabilities into customer-facing offerings.

Build and maintain scalable data platforms, pipelines, observability metrics, metadata-driven solutions, data transformations, and third-party integrations. Collaborate with data scientists, analysts, DevOps teams, and business stakeholders to deliver timely, accurate data and meaningful insights. Responsibilities include optimizing transformations, implementing infrastructure as code, and administering or tuning Snowflake environments.

Own the strategy, governance, architecture, modeling, and reporting foundation for People Experience data. Build governed metrics, semantic and consolidated data layers, dashboards, data catalogs, quality monitoring, and operating processes from the ground up. Manage intake, prioritization, access, privacy, enablement, and self-service adoption while partnering with stakeholders on analytics and future AI capabilities. The role combines strategic program leadership with hands-on SQL, data modeling, reporting, and team mentoring.

Build and maintain scalable data platforms, pipelines, observability metrics, metadata-driven solutions, data transformations, and third-party integrations. Collaborate with data scientists, analysts, DevOps, and business stakeholders to deliver accurate, timely data and meaningful insights. Responsibilities include optimizing workflows, implementing infrastructure as code, and supporting cloud-based data systems.

Owns the end-to-end architecture, development, infrastructure, data pipelines, and Power BI reporting platform supporting data governance, informatics, analytics, auditing, and data quality. Leads and mentors a data engineering team, defines schemas and engineering standards, ensures security and HIPAA compliance, oversees testing and Agile delivery, manages stakeholder requirements, and remains hands-on with backend technologies, data models, Power BI, and DAX.

Perform reserving, pricing, and predictive modeling for personal lines insurance. Support regulatory filings, provide data-driven insights for product decisions, automate processes, and collaborate with business partners to forecast risk and inform strategy.

Design and maintain healthcare data integration pipelines, models, and validation processes for claims, billing, clinical, pharmacy, EMR, FHIR, and value-based care data. Collaborate with clients and internal analytics, product, and customer success teams to interpret data, document business rules, support implementations, and build reusable Ursa data models. Apply healthcare domain expertise, data security practices, HIPAA compliance, and appropriate judgment when using AI-assisted tools.

Build and maintain scalable monitoring solutions and dashboards to track production, campaign, and operational data. Analyze large-scale datasets to detect anomalies, perform root-cause investigations with Engineering and Production Operations, and deliver actionable insights to improve platform reliability and operational efficiency.

Lead enterprise data strategy and governance to establish semantic architecture, metadata, lineage, stewardship, and standards enabling trusted data for AI, analytics, and operations. Define operating model, policies, reference architectures, and roadmaps while partnering with Legal, Compliance, Security, Technology, and business leaders to ensure regulatory compliance and enterprise-wide adoption.