Top Remote Data & Analytics Jobs in Dayton, OH

Provide research support and program evaluation for public health projects: analyze mixed-methods data, produce reports, link implementation measures to outcomes, and advise stakeholders. Use BI and qualitative tools, manage administrative/government systems, and communicate findings to high-level officials.

Design, build, and maintain end-to-end data pipelines and ETL/ELT for healthcare data using Snowflake, dbt, Python, and SQL. Ensure data quality, monitor production jobs, troubleshoot anomalies, write dbt models and tests, collaborate with Analytics/Operations/Clinical teams, and participate in Agile processes with GitLab and Jira.

Design, build, and maintain scalable cloud-based data pipelines and lakehouse platforms. Integrate diverse business systems, ensure data quality, support analytics and AI, implement governance, and mentor junior engineers while optimizing performance and enabling enterprise reporting and ML use cases.

Analyze marketing and customer data to identify trends and optimization opportunities. Build scalable lead ingestion solutions, write SQL and Python for ETL and analysis, maintain curated datasets and dashboards, automate reporting, and collaborate with marketing, analytics, data science, and engineering teams while documenting data definitions and methodologies.

Senior member of the Data & Analytics team who translates operational reporting requirements into Epic- and Qlik-based analytics solutions. Develops RW SQL reports, Radar dashboards, and self-service artifacts; validates data, mentors junior analysts, enforces best practices, supports intake/prioritization, and coordinates training and legacy-to-new-tool migrations.

Perform data analysis on complex spreadsheets, benchmark and research data to identify trends and insights. Collaborate with writers, engineers, and strategists to validate and implement findings, create charts and visualizations, and deliver timely, client-facing deliverables as a US-based freelance contractor.

Build and maintain reports, dashboards, and visualizations; analyze data to provide actionable insights; support ad hoc requests; ensure data quality with engineers; document processes; integrate AI tools to streamline cleaning, analysis, and documentation; collaborate with stakeholders to track outcome-based metrics.

Builds and manages ETL processes and data pipelines in Azure Databricks using PySpark. Integrates data from multiple sources, tunes Spark and SQL for performance, troubleshoots pipeline failures, automates manual processes, and supports reporting and analytics to inform business decisions.

Lead and scale the enterprise data hub: manage the data warehouse, build reliable ingestion and transformation pipelines, implement governance and data modeling best practices, deliver GTM-ready metrics, enable AI/ML use cases, prioritize the data roadmap, and lead/mentor a data engineering team.

Own and evolve the data platform: architect, build, and maintain data models, pipelines, and access layers. Ensure data quality, governance, and reliability; monitor and optimize infrastructure; provide technical leadership, code reviews, and collaborate with cross-functional teams to deliver scalable data solutions for operations and analytics.