Top Remote Data & Analytics Jobs in Dayton, OH

Investigate and resolve data integration, file processing, and transfer issues. Monitor automated jobs, use SQL and SSIS to validate data, manage SFTP workflows, reprocess files, document integration processes, and collaborate with internal teams to ensure accurate and reliable client and vendor data exchanges.

Modernize an Azure Synapse platform and migrate workloads to Microsoft Fabric. Build metadata-driven ingestion frameworks, Fabric pipelines, Spark notebooks, validation and reconciliation processes, and Git-based CI/CD workflows. Analyze complex Synapse dependencies, implement full-load, incremental, and CDC patterns, and support medallion architecture, governance, lineage, monitoring, and security. Collaborate with client engineers through paired development, architecture discussions, code reviews, and knowledge sharing while using Claude Code for AI-assisted development.

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 ETL processes, data warehouses, dashboards, analytics models, and reporting solutions. Develops pricing optimization algorithms, machine learning implementations, automated data processes, and self-service analytics tools using R, Python, SQL, and cloud technologies. Leads projects, mentors developers, documents systems, develops technology roadmaps, and supports production database performance and maintenance.

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.

Develops and maintains enterprise Power BI solutions, including dashboards, reports, semantic models, data transformations, SQL queries, row-level security, refreshes, gateways, and deployments. Translates business requirements into scalable analytics solutions, optimizes performance, troubleshoots data and connectivity issues, supports testing and documentation, and communicates findings to technical and non-technical stakeholders. Provides guidance to junior staff while collaborating across business, data, development, and architecture teams.

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