Top Analysis Reporting Jobs in Jacksonville, FL

Supports NOAA Fisheries data integration through source-to-target mapping, data quality analysis, interoperability testing, compliance validation, metadata documentation, requirements analysis, dashboard specifications, and operational documentation. The role bridges data engineering and regulatory compliance for a federal environmental data integration program.

Build and optimize business intelligence dashboards, reports, data models, visualizations, and calculations using tools such as Power BI, Grafana, Tableau, and DAX. Develop data ingestion pipelines, support governance and access controls, analyze diverse datasets, and deliver tailored analytics solutions. Collaborate with customers, consultants, architects, engineers, and data scientists using a consultative approach across multiple industries.

Design, build, and deploy production-grade data pipelines, analytics platforms, and Generative AI applications on Databricks. Lead enterprise migrations from legacy data platforms, architect cloud-native solutions, implement RAG and ML pipelines, and advise executive client stakeholders. Drive Databricks adoption and account growth by identifying new use cases, while mentoring engineers and contributing reusable delivery accelerators.

Build scalable Databricks Lakehouse architectures and ETL/ELT pipelines using Spark and Delta Lake across AWS, Azure, and GCP. Tune performance and cloud costs, integrate MLflow for AI model deployment, and implement CI/CD and Infrastructure-as-Code with Terraform or Databricks CLI. The role also enforces governance and security through Unity Catalog, supports regulatory compliance, and collaborates with clients, data scientists, architects, and engineering teams to deliver production-ready AI solutions.

Reviews and compares AI chatbot responses using predefined criteria for accuracy, relevance, completeness, clarity, consistency, and quality. Documents evaluation guidelines, analyzes workflows and data, identifies recurring issues, prepares reports, tracks quality metrics, and collaborates with project teams. This fully remote, 16-week contract requires 8 hours per day, five days per week, with Pacific Time overlap and possible weekend work.

Leads the architecture and evolution of scalable data platforms, pipelines, and data products across multiple teams. Designs Snowflake and cloud-based solutions for batch, streaming, CDC, ingestion, transformation, orchestration, and data delivery. Establishes engineering standards, governance, observability, testing, CI/CD, and reliability practices; mentors engineers; guides architecture and roadmap decisions; resolves complex incidents; and evaluates AI, automation, and MCP-enabled data capabilities.

Own analytics as an internal data product across GTM, People, Finance, and related functions. Translate ambiguous business questions into self-service data solutions, deliver executive-ready narratives, manage stakeholder priorities, and guide data-driven decisions. Serve as a marketing analytics subject matter expert across attribution, campaign measurement, lead tracking, funnel analysis, predictive modeling, and experimentation. Use SQL, modern data-stack tools, BI platforms, and AI-assisted modeling to deliver actionable insights.

Analyze large healthcare datasets, build and implement quantitative models, perform statistical analyses, ensure data accuracy, collaborate with cross-functional teams, and provide actionable insights to improve healthcare delivery.

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).

Design, build, and maintain Power BI dashboards and data models using Snowflake and other cloud sources. Collaborate with data engineers/ETL teams, gather stakeholder requirements, implement DAX and Power Query transformations, ensure data governance and performance, and deliver analytics within Agile processes.

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.