Top Senior Analysis Reporting Jobs in Tallahassee, FL

Develops and maintains Salesforce and Tableau analytics dashboards, performs complex data analysis and forecasting, and delivers insights for sales, product, finance, and marketing decisions. Translates business needs into technical requirements, manages platform enhancements with developers, validates data quality, models sales goals, identifies optimization opportunities, and presents strategic recommendations. Provides user training and supports analytics across Treasury Solutions.

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.

Leads the design, development, support, and governance of enterprise-wide business intelligence and decision-support systems. Partners with senior business and IT leaders to prioritize data requirements, solve complex technical problems, optimize BI performance, establish testing and quality standards, and research new technologies. Leads projects, develops business cases, trains customers, and mentors junior technical staff.

Leads the design and implementation of advanced data pipelines, architectures, and scalable cloud-based data solutions. Defines technical approaches and success metrics, monitors production reliability, and collaborates with stakeholders, data scientists, and analysts to optimize workflows. Provides technical mentorship, develops reusable assets and best practices, and supports enterprise data strategy. The role requires database administration, SQL and Python expertise, data warehouse modeling, visualization tools, and preferably GIS, Snowflake, PostgreSQL, orchestration, and big data experience.

The Business Analyst will analyze business processes, document requirements, test applications, and coordinate with project teams to develop information solutions.

Lead client-facing marketing analytics projects using quantitative methods (marketing mix modeling, multi-channel attribution, digital analytics, pricing and demand forecasting). Provide analyses, recommendations, presentations, and advice; manage and grow a team; perform customer-level analysis using SAS and other statistical programming tools. Frequent travel required.

Design and build petabyte-scale multimodal data pipelines and infrastructure (text, audio, images, video). Own and maintain systems like Spark, Ray, and vector databases, partner with pre/post-training teams to improve data recipes through experimentation, and support large-scale dataset storage and processing for AI model training.

Build and operate batch and near-real-time data pipelines, semantic models, certified measures, APIs, and serving infrastructure for AI-powered decision systems. Responsibilities include data quality, observability, lineage, governance, CI/CD, infrastructure as code, performance optimization, model scoring pipelines, and AI telemetry. Partner with data scientists and business stakeholders to productionize models and deliver reusable, documented data platform capabilities.