Top Data & Analytics Jobs in Tallahassee, FL

Lead a remote analytics team to deliver dashboards, data products, and decision-support tools. Oversee project plans, stakeholder engagement, data platform modernization, ETL/automation, Tableau/Power BI deployments, statistical modeling, QA, metadata, and staff mentoring to support federal social service programs.

Support data collection, analysis, and reporting for a government care program. Develop data collection tools, identify performance indicators, conduct analysis and visualization (Power BI/Tableau), prepare summaries and presentations, train staff, oversee report quality, and help build data repositories and ETL processes.

The Manager of Solutions Engineering will lead a team of Solutions Engineers, focusing on customer engagement, technical demonstrations, and mentoring while enhancing POC success.

The Data & Analytics Leader will develop a multi-year roadmap to enhance data governance and analytics capabilities, while fostering a data-driven culture throughout ERAC.

Design, build, and operate secure, compliant data platforms and pipelines across AWS GovCloud and Azure Government. Lead cloud migrations, implement DataOps CI/CD, manage Microsoft Fabric platform, ensure data quality and governance (NIST 800-171/DFARS/ITAR), and provide Tier 3 support for analytics workloads. Collaborate with stakeholders to deliver scalable lakehouse/warehouse architectures, automate infrastructure with IaC, and maintain compliance and incident runbooks.

Senior Data Architect contractor to accelerate and modernize the data platform: assess landscape, design end-to-end data solutions, define integration and metadata patterns, enable data mesh and cloud migration (AWS), and produce reusable architecture artifacts for handoff to engineering teams.

Support fundraising through CRM and database management, develop queries/reports/dashboards, analyze constituent and fundraising data, maintain prospect pipelines, and coordinate moves management and portfolio reviews with development officers and prospect researchers to ensure accurate CRM records and effective prospect assignment.

Build and maintain BI dashboards and KPI reporting for manufacturing, supply chain, quality, and fulfillment. Create internal tools and workflow automations, integrate APIs and business systems, and consolidate ERP/MES/PLM data into unified operational reporting tied to recurring decisions.

Build, maintain, and validate production data pipelines and ETL infrastructure (BigQuery/GCS/dbt/Airflow alternatives). Ensure data observability, SLAs, and automated tests to deliver reliable datasets that support campaign decision-making and organizers. Collaborate with product and data teams across fast-paced, cycle-driven work.

Lead end-to-end product analytics: instrumentation, data quality, funnels, retention, and experimentation. Use SQL and Python for analysis, build dashboards, partner with product and growth, and improve analytics infrastructure (Amplitude and data warehouse migration). Remote with GMT+1 overlap.

Lead design and implementation of scalable cloud data lakes, warehouses, and marts. Build ELT/ETL pipelines, apply Data Vault 2.0 and dimensional modeling, implement data quality, lineage, observability, and governance. Optimize Snowflake/Databricks/Synapse platforms, champion CI/CD and DataOps, mentor engineers, and enable AI/ML-ready data products through collaboration with architecture and product teams.

Design, build, and operate scalable cloud data pipelines and platforms (Azure preferred). Implement DevSecOps, CI/CD, automated testing, monitoring, performance tuning, and production readiness. Mentor engineers, drive operational excellence, and translate requirements into robust technical designs. Apply healthcare domain knowledge and collaborate in agile teams to deliver secure, reliable data products.

Lead design and delivery of scalable, secure cloud data pipelines and products. Translate requirements into architecture, apply advanced data modeling (Data Vault 2.0/dimensional), embed data quality, observability, security, and DataOps/CI-CD practices, optimize performance and cost, mentor engineers, and enable AI/ML-ready datasets within an agile DevSecOps model.