Top Remote Data & Analytics Jobs in Dayton, OH

The Senior Data Engineer will design and build scalable data platforms, handle automation for data operations, and ensure reliability and performance of data pipelines while collaborating with various teams.

Owns analytics and reporting that inform sales, merchant success, product, and fraud teams; builds business cases, pricing models, dashboards, and ROI analyses to drive merchant acquisition, expansion, and retention.

Synthesize analyses across product, marketing, reporting, and data science to create executive-ready narratives and decision materials. Partner with analytics teams and business leaders to surface high-impact insights, improve discoverability and adoption of analytics, set standards for storytelling, and ensure insights drive organizational alignment and action.

Design, build, and maintain automation workflows and data pipelines across admissions, education, career services, and billing. Create BI dashboards, ensure data accuracy, manage lightweight databases (e.g., Supabase), and support cross-functional teams to improve operational efficiency and scalability.

Lead analytics for member engagement and revenue recognition: build and validate Braze attributes, run attribution and A/B test analyses, maintain Looker dashboards, join campaign and engagement data via SQL/DBT, and provide actionable recommendations to Member Operations and Marketing.

Partner with stakeholders to gather requirements, validate integrated datasets, and deliver trusted insights via SQL-driven analysis and Tableau/Qlik dashboards to support federal healthcare operational decisions.

Design, implement, and maintain dbt/Snowflake ELT transformations to convert raw clinical and multimodal health data into trusted, analysis-ready models; enforce data quality, lineage, testing, CI/CD, and documentation; collaborate with clinical and ML teams to harmonize ontologies and support de-identification and ETL across health systems.

Analyze, clean, and validate data from multiple sources; develop and run SQL queries; calculate KPIs and financial metrics; create visualizations and present insights to non-technical stakeholders; collaborate with business partners to translate requirements into data-driven recommendations.

Lead and grow a data engineering team, set technical standards and roadmap, architect scalable GCP data solutions (ingest, transform, warehouse, serving), ensure pipeline reliability and data quality, partner with analytics and client teams, drive best practices (CI/CD, IaC, governance), and evaluate new tooling.

Design, deploy, and govern enterprise-scale data architectures and platforms to support analytics and AI. Define modeling standards, integration pipelines, data quality, lineage, and security. Lead technical direction, mentor engineers, evaluate AI tooling, and support proofs of concept and solution roadmaps that enable generative AI and RAG solutions.