Top Senior Data & Analytics Jobs in Jacksonville, FL

Lead and grow the Revenue Analytics team to deliver strategic sales and customer lifecycle insights. Build forecasting, pipeline, capacity, predictive models, and customer lifecycle analytics to improve revenue, retention, adoption, and expansion. Partner with GTM, Finance, Product, and Enterprise Data to create executive reporting, data governance, AI-enabled self-serve datasets, and integrated analytics that drive business outcomes and support a shift to consumption-based models.

Own Silver and Gold medallion-layer modeling and semantic layer governance using dbt and SQL. Improve warehouse performance and cost, standardize BI tooling, collaborate with stakeholders to produce trusted metrics, troubleshoot data quality, and evolve analytics platform.

Partner with People teams to turn HR and org data into actionable insights. Build complex queries, statistical analyses, dashboards, and a centralized people data repository. Drive data governance, data quality, and stakeholder communication to support hiring, retention, productivity, and workforce decision-making.

Owner of the analytics foundation: design and maintain BigQuery/dbt models, pipelines, and Looker Studio reporting; build AI workflows and web apps on GCP; integrate marketing, e-commerce, and fulfillment data; ensure GCP ops, security, and reliability; use AI-assisted tools daily and collaborate with business and client stakeholders.

Analyzes fraud and account takeover risks using SQL, SAS, Excel, Tableau, and relational databases. Develops and maintains fraud detection rules, mitigation controls, monitoring processes, performance reports, and segmentation strategies. Investigates major fraud losses and emerging trends, identifies root causes, recommends policy improvements, and provides actionable insights to cross-functional stakeholders and senior management. The role focuses on minimizing fraud losses while reducing customer friction and improving operational performance.

Designs, develops, tests, documents, and deploys scalable data architectures, big data platforms, ETL pipelines, and data warehouse solutions. Improves process automation, infrastructure scalability, data reliability, and quality. Troubleshoots complex data issues, performs validation and performance tuning, and collaborates with developers, architects, analysts, data scientists, and functional teams to deliver business-aligned data solutions.

Leads data mapping and conversion initiatives for client onboarding, serving as a subject matter expert on schemas, financial balancing, and data transmission. Resolves data discrepancies, supports tax processing and implementation teams, develops scripts to optimize data loading, and ensures delivery against release timelines. The role requires strong SQL Server expertise, analytical and communication skills, project management capabilities, and financial trust accounting knowledge.

Performs independent quality assurance and validation across enterprise data platforms, warehouses, integrations, reporting solutions, and business processes. Responsibilities include data quality analysis, test strategy and automation, regression and release validation, incident investigation, root cause analysis, requirements traceability, governance, regulatory validation, and monitoring data quality metrics. The role partners with engineering, business, compliance, and operational teams while improving enterprise data integrity, reliability, and governance.

Lead advanced analytics for pricing and underwriting by building models, curated datasets, pipelines, dashboards, and data products. Partner with Pricing, Underwriting, IT and leadership to prioritize initiatives, deploy scalable analytics on cloud platforms, evaluate emerging technologies/AI, and mentor a small analytics team to improve risk selection, portfolio management, and pricing accuracy.

Lead design and implementation of a scalable enterprise data warehouse: dimensional modeling, ETL/ELT pipelines, cloud data platforms, data governance, performance tuning, monitoring, and mentoring. Collaborate with stakeholders and engineering teams to enable analytics, ensure data quality and security, and support data science/ML workloads including feature engineering and NLP components.