Top Data & Analytics Jobs in Dayton, OH

Investigate fraudulent accounts, payouts, traffic sources, and partner activity across rewarded mobile apps. Analyze fraud patterns and MMP data, build risk rules and verification controls, quantify financial impact, and report results. Partner with Product, Engineering, Growth, Support, and external partners to prevent fraud while minimizing false positives. The role also establishes review standards, mentors teammates, evaluates fraud tools, handles appeals, and helps develop AI-assisted fraud workflows.

Owns the organization’s business intelligence environment, including Power BI reports, dashboards, datasets, refreshes, workspaces, access, and data pipelines. Integrates clinical, billing, and vendor data into a central warehouse, manages vendor data exchanges, establishes KPIs, and presents actionable findings to internal teams and executives. Requires strong SQL, Power BI and DAX expertise, AI-assisted development skills, API and integration experience, C# literacy, and at least three years supporting production data and reporting systems.

Analyzes and processes large-scale assessment datasets, maintains psychometric software and statistical code, troubleshoots programs, develops quality controls, coordinates data transfers, supports standard-setting studies, and communicates results. The role also improves workflows, mentors colleagues, supports project planning, and collaborates with psychometricians and technical teams.

Build and maintain scalable, fault-tolerant data pipelines, warehouses, and analytics solutions. Transform complex structured and semi-structured data, automate delivery processes, improve data quality and reliability, and support enterprise data strategies. Collaborate with analysts, data scientists, actuaries, and business teams to translate requirements into solutions. Research new technologies, troubleshoot issues, maintain version-controlled code, and mentor data engineering team members.

Designs and maintains scalable data models, semantic layers, ETL/ELT workflows, data-quality frameworks, and governed analytics products for commercial operations. Owns data solutions from requirements through deployment, monitoring, documentation, and support. Partners with technical and business stakeholders to deliver trusted reporting, dashboards, scorecards, productivity measurements, and AI-enabled decision support.

As a Revenue Analytics Manager, you will own reporting for Sales, Marketing, and Customer Success, build dashboards, automate workflows, and ensure data quality.

Serves as the technical authority for Azure SQL architecture, deployment, security, performance, operations, and migrations supporting federal programs. Designs secure Azure Government database environments, leads on-premises SQL Server migrations, develops T-SQL and data models, builds Azure Data Factory pipelines, implements DevOps and infrastructure automation, troubleshoots incidents, optimizes costs, documents standards, and mentors team members. Collaborates with engineering, architecture, data, and DevOps teams on reliable data platforms.

Lead design and implementation of tracking with GTM, analyze user behavior in GA4, build BigQuery queries and dashboards, define KPIs with cross-functional teams, troubleshoot tagging/data issues, and perform deep-dive conversion and journey analyses to optimize web and mobile performance.

Supports clients and internal stakeholders by gathering requirements and developing business intelligence, reporting, automation, and low-code/no-code solutions. Creates SQL-based data solutions, reports, dashboards, and visualizations; troubleshoots data and automation issues; and collaborates with infrastructure and development teams to maintain solution stability, performance, and security.

Build and maintain reliable data pipelines supporting money movement, fraud response, and client reporting. Integrate internal and external APIs, orchestrate workflows with Airflow on Kubernetes, and implement testing, monitoring, alerting, and observability. Debug production issues, document root causes, improve pipeline efficiency, collaborate with stakeholders, and contribute to technical designs. The role requires Python, SQL, Airflow, Snowflake, dbt, and GCP experience, with opportunities for increased end-to-end ownership.