Top Data & Analytics Jobs in Jacksonville, FL

Design and maintain dashboards and visualizations, perform deep-dive analyses to ensure reporting accuracy, and consult with stakeholders on data strategy. Wrangle and combine complex datasets, develop KPIs and sales performance metrics, and research data issues to recommend solutions. Prepare and present insights to executives and liaise with technology teams to implement data requirements and governance.

Lead design and implementation of data architecture for healthcare systems, influence stakeholders, mentor teams, ensure regulatory compliance, enable data integration and transformation, and apply AI techniques to improve healthcare operations and reporting.

The Senior Business Operations Analyst will focus on data-driven insights and operational strategies for Collections operations. Responsibilities include SQL analysis, Tableau reporting, and collaborative optimization with various teams to enhance efficiency and decision-making.

Support asset-level data intake, validation, and exception triage for private markets. Ensure data quality and standardization, perform trend analysis to inform product and process improvements, coordinate onshore/offshore delivery, document operational logic, and collaborate with Product, Data, and Engineering teams to improve workflows and client reporting.

Build and operate an enterprise Databricks lakehouse: design and maintain Bronze and Silver ingestion/transformation pipelines (batch/stream/CDC), implement Unity Catalog and secure platform constructs, create CI/CD for data assets, enforce data classification and quality, and collaborate with domain teams to enable governed data for analytics.

Own and design the Databricks Gold and enterprise semantic layer: implement governed facts, dimensions, KPIs, and semantic models. Translate business KPI definitions into auditable transformation logic, enforce governance promotion paths, review models for correctness, coach domain teams, and ensure traceability, documentation, and compliance-aware data exposures for BI and self-service analytics.

Lead KPI reporting, funnel analysis, forecasting, and business diagnostics for Enterprise Growth across Coursera + Udemy. Build unified definitions and durable self-serve reports, support experiments and inside-sales/outsourced partner evaluation, and provide analytical inputs for target setting and prioritization to drive growth and conversion improvements.

Design, build, and maintain scalable dimensional data models, semantic layer, and production transformation pipelines. Collaborate with Data Engineering, Data Science, BI, DevOps, and Product to enable AI-ready data, ensure data quality and governance, support ML feature delivery, and coach analytics team members for robust, production-ready data products.

Lead and grow a team of analytics engineers while remaining hands-on with SQL, dbt, and Snowflake. Own roadmap and semantic layer development, build trusted, governed data products, partner with cross-functional stakeholders, champion data quality and observability, and hire and coach engineering talent to deliver scalable analytics and AI-ready foundations.

Lead technical design and architecture for analytics engineering: build and govern the semantic/metrics layer, optimize dbt and Snowflake for performance and AI use cases, mentor engineers, partner with Data Science and Engineering, drive data quality, CI/CD, observability, and deployment of third-party data and emerging tooling to support analytics, ML, and agent-based products.

Lead and grow a data engineering team that builds and operates ingestion, transformation, orchestration, and serving layers and a self-serve analytics substrate. Own data quality, observability, cost efficiency, SLAs, and engineering practices while partnering with Data Science, BIE, Analytics, and Product to deliver reliable, reusable data products.

Lead design and scaling of the data platform and analytics pipelines. Build and maintain ETL/ELT workflows, integrations, serverless data services, and reliable data models. Mentor teammates, improve automation, observability, and data governance to support analytics and operational insights.

Lead independent validation and governance of credit and fraud models, build automated Python monitoring for model drift and performance, drive remediation with model developers, and liaise with audit and regulators to support Affirm Bank's model risk management.