Top Senior Analysis Reporting Jobs in Dayton, OH

As a Senior Data Engineer, you will enhance the data lakehouse, optimize data workloads, perform complex transformations, and develop frameworks to improve delivery velocity, focusing on cybersecurity solutions.

The role involves managing actuarial modeling for Retirement Plans, ensuring quality of outputs, overseeing BAU execution, and training junior staff.

The Data Product Manager defines the reporting roadmap, manages KPI alignment, collaborates cross-functionally, ensures data quality, and leads continuous improvement in analytics processes.

Analyze revenue and growth performance, provide insights on auction behavior, measure A/B tests, define KPIs, and perform root-cause analysis.

The Senior Pricing and Paywalls Product Analyst will automate analytical data collection, analyze product metrics, organize A/B tests, and support the generation of product hypotheses to identify growth opportunities.

Lead global partner analytics and planning for partner GTM motions. Build dashboards, business models, AI-enabled workflows, and executive reports to support partner planning, incentives, coverage, performance management, and cross-functional decision-making across hyperscalers, ISVs, marketplaces, and reseller channels.

Lead data-focused business analysis: elicit and document requirements, model data and processes (As-Is/To-Be), design data flows, support integrations, migrations, EDW/reporting decisions, mentor team members, and drive data governance and cataloguing across enterprise systems.

Lead enterprise analytics initiatives across Sales, Marketing, and Operations. Build predictive and statistical models, create KPI dashboards, translate data into executive-ready insights, mentor junior analysts, and partner with stakeholders and data engineering to deliver actionable recommendations that improve acquisition, retention, and channel performance.

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.

Lead architecture and implementation of scalable data systems and pipelines from ingestion to delivery. Own ambiguous, high-impact integrations, improve data quality and reliability, adopt AI-augmented engineering, mentor engineers, and collaborate with Product, ML, and customer teams to ship production solutions.