Top Senior Analysis Reporting Jobs in Dayton, OH

Lead demand forecasting and performance analytics for eCommerce: build forecasting models, run scenario and promotional analyses, produce executive reporting, drive cross-functional predictive initiatives, and deliver market and competitive insights to inform planning and resource allocation.

Lead catastrophe risk analytics across lines, validate and customize Cat models, collaborate with underwriting/actuarial/finance to inform pricing and portfolio strategy, build Cat modeling workflows, mentor analytics staff, support live catastrophe events, and drive R&D on emerging risks and vendor/model calibration.

Own and maintain KPI reporting pipelines and business logic, ensure data quality, diagnose KPI changes, perform deep-dive analyses, build dashboards and visualizations, architect data models and curated reporting zones, and partner with Data Engineering and product/marketing stakeholders to enable self-service analytics.

Serve as embedded BI lead for a divisional sales book, delivering actionable insights from syndicated and commercial data, leading opportunity analysis, training field teams, supporting Power BI reporting, and translating complex data into executive presentations to drive sales performance and planning.

Lead design and implementation of end-to-end ELT pipelines and transformation layers (dbt) to produce curated, reporting-ready datasets. Define data models, governance, metadata, lineage, and engineering standards. Partner with cross-functional teams to translate requirements, optimize performance and costs (cloud DW like Snowflake), and grow a data architecture practice that supports BI, self-service analytics, and AI agents.

Lead business intelligence efforts to analyze complex data, define KPIs, identify operational inefficiencies, and recommend process improvements. Collaborate with cross-functional teams and executives to implement data-driven strategies, manage improvement projects, monitor resource allocation, and ensure compliance with policies and regulations.

Design, build, and scale reliable batch and real-time data pipelines and warehouses. Partner with engineering, data science, and business teams to define data models, improve observability, optimize storage and querying, and evangelize data engineering best practices across the company.

Design, build, and maintain scalable Databricks-based data pipelines and ELT workflows; develop and document dbt models; define data modeling methodology; ensure data quality, reliability, and scalability; collaborate with clients and cross-functional teams; provide technical guidance and deliver improvements to clients' data infrastructure and analytics capabilities.

The Solutions Consultant will conduct product demos, assist in sales cycles, and provide evidence of product value while maintaining product knowledge and client relations.

The Product Analyst will measure results and deliver insights on new AI products, identify opportunities, and guide product strategy through data analysis and visualization.