Top Senior Analysis Reporting Jobs in Orlando, FL

Build, validate, deploy, and maintain forecasting, optimization, simulation, predictive, and cost-assessment models for digital assessment operations. Develop analytics pipelines, dashboards, and decision-support tools; improve operational efficiency, capacity planning, customer experience, and cost management. Partner with Product, Program, Operations, Technology, and Finance teams to translate analysis into decisions, shape the analytics roadmap, and identify new modeling opportunities.

Lead end-to-end analytics for Netflix Ads demand, including media planning and campaign management tools, machine learning models, and agentic solutions. Partner with scientists, product managers, engineers, and data engineering to develop metrics, reliable analytics data, insights, and recommendations. Independently identify risks and opportunities, shape product roadmaps, and communicate findings to technical and non-technical stakeholders.

Build and maintain branch network performance reporting, dashboards, semantic models, and analytical datasets using SQL and Alteryx. Translate banker, branch, market, and enterprise data into actionable insights for senior leaders. Lead analytical projects, manage timelines, communicate executive-ready findings, improve reporting processes, and mentor junior analysts. Partner with Distribution and Consumer Bank leadership in a hybrid, primarily onsite environment.

The Workday Reporting and Security Analyst develops and maintains Workday reports, dashboards, calculated fields, and People Analytics to deliver workforce insights. The role gathers reporting requirements, validates data, troubleshoots issues, conducts release testing, and documents solutions. It also administers Workday security roles and policies, audits access, enforces privacy and SOX controls, supports internal and external audits, and partners with HR, Finance, IT, vendors, and business stakeholders.

Leads data engineering teams in designing, developing, and supporting scalable batch and streaming data solutions. Responsibilities include data pipeline development, architecture, governance, security, platform optimization, production support, technical mentorship, stakeholder collaboration, and delivery leadership. The role requires hands-on engineering with Python or similar languages, modern data technologies, cloud platforms, DevOps practices, and people-management responsibilities.

Own Hercules’ full data stack, including ETL from third-party providers into BigQuery, data modeling with dbt and medallion architecture, semantic-layer metric definition, and self-service business intelligence in Omni. Build reliable, maintainable systems and dashboards while applying strong software architecture, API design, code quality, and AI-assisted development practices.

Develops scalable data and software systems using Python and Apache Spark. Designs APIs, data models, ETL workflows, orchestration, and data interactions while optimizing performance, scalability, integrity, and security. Builds user interfaces, contributes to automated testing, applies Agile practices, and supports CI/CD delivery. The role requires strong software engineering, distributed computing, analytical, and communication skills, with AWS, R, Databricks, and SAS experience preferred.

Develop and optimize SQL Server and SSIS billing pipelines, automate data movement and validation with Python, and use R and Python for analytics, anomaly detection, and billing decision models. Build data-quality frameworks, support internal reporting tools, integrate ERP APIs, and collaborate with finance and operations stakeholders. The role requires maintainable, tested, auditable solutions and ownership of features across databases, pipelines, analytics, and internal applications.

Own marketing channel performance tracking, reporting, experimentation, dashboards, forecasting, and LTV analysis. Develop measurement frameworks, identify data issues, and translate complex findings into actionable recommendations for Marketing, Growth, Product, and Finance stakeholders. The role requires strong SQL, BI dashboarding, marketing measurement, experimentation knowledge, and effective analytical communication, with AI tools used to accelerate reporting and analysis.

Design, build, deploy, and maintain AWS-based data and AI/ML infrastructure for enterprise agentic AI platforms. Develop data lake pipelines, observability systems, agent-enablement tooling, and production MLOps/LLMOps capabilities. Own features end-to-end, contribute to architecture and design decisions, optimize reliability, scalability, security, and cost, and collaborate with AWS Professional Services, client teams, and junior engineers.