Top Remote Analysis Reporting Jobs in Cincinnati, OH

Partner with GTM leaders to turn large datasets into actionable insight, build dashboards and forecasting models, analyze pipeline/retention/productivity, and implement automation and AI-enabled workflows to improve revenue performance and GTM operations.

The Business Intelligence Analyst analyzes complex data for finance teams, develops reports and dashboards, and enhances financial reporting solutions. They collaborate with teams to ensure reporting accuracy and alignment with business needs.

Design, build, and optimize scalable batch and streaming data pipelines. Model and process structured and unstructured data, ensure production reliability and observability, apply ML techniques for insight extraction, and drive data strategy while adopting modern tooling and IaC practices.

Build, maintain, and improve AI-driven automations and data systems. Own Power BI dashboards and underlying pipelines (Python, SQL, DAX), manage complex margin/profit-share processes, create low-code AI automations (Claude, Zapier, Make), ensure data governance, and document/support solutions for company-wide use.

Design and analyze experiments, measure campaign incrementality, identify customer segments and engagement opportunities, validate tracking and anomalies, surface actionable insights, and build scalable measurement infrastructure with cross-functional partners.

Produce and validate default reporting and analytics for mortgage servicing portfolios. Investigate data discrepancies, perform root-cause analysis, modify reports for ad-hoc needs, document processes, and collaborate with default management to ensure regulatory and investor-compliant reporting.

Build and maintain revenue and customer-lifecycle reporting, author complex BigQuery SQL, develop segmentation and clustering models in BigQuery ML, perform entity resolution and data quality reconciliation, migrate and maintain Tableau dashboards, and deliver ad-hoc and recurring analyses to support Sales, Revenue, and cross-functional teams.

Build and own data platform infrastructure and scalable pipelines (ingestion, orchestration, transformation, delivery). Define governance, observability, and AI-first architecture. Optimize performance, ensure data quality, mentor engineers, and integrate AI-assisted tooling.

Lead fraud analytics for application fraud detection: identify emerging patterns, design and test real-time defenses, balance risk and customer experience, build dashboards and metrics, and collaborate with Operations, Product, Data Science, and Engineering to reduce fraud losses and improve cardholder experience.

Design, build, and operate scalable PostgreSQL databases and cloud-native data infrastructure. Build and maintain ETL/ELT pipelines, optimize query and server performance, ensure high availability and disaster recovery, improve observability, troubleshoot production issues, guide schema and application design, evaluate database technologies, and mentor junior engineers to support reliable, secure data-driven applications.