Top Analysis Reporting Jobs in Dayton, OH

Lead design and optimization of analytics products and Tableau dashboards, manage and tune reporting data pipelines (Redshift/ETL), implement performance best practices, collaborate cross-functionally, and drive automation and AI opportunities to improve reporting and analytics for healthcare customers.

Design, implement, and optimize AWS-based data infrastructure and automated ETL/ELT pipelines using Airflow, DMS, Glue, Redshift, and Athena. Build Terraform modules and CI/CD for data workflows, enforce security/IAM, implement observability, optimize performance/cost, and mentor junior engineers.

The Data and Analytics Consultant will design, develop, and implement data reporting, visualizations, and analytics solutions for clients while assisting them in data understanding and value extraction.

The Manager will lead client projects in Data and Analytics, focusing on strategy formulation, process automation, and data visualization, collaborating with various stakeholders.

Lead a team of Data Scientists and Analysts, developing AI and data-driven solutions, driving analytics insights, and fostering collaboration across teams.

Build production-quality data pipelines and analytical workflows using Python, SQL and Spark; clean, transform and analyze structured data; validate and deploy predictive models; support client presentations and explain results to non-technical audiences; contribute to and improve the firm’s analytics platform and tooling.

Lead oversight of data management for assigned studies, manage external vendors, review and approve DM documentation, perform secondary UAT of EDC, implement RBQM oversight, and provide cross-functional input on protocols and study deliverables to ensure data quality and regulatory compliance.

Lead design and delivery of enterprise data centralization and automation for reporting and BI. Build and maintain ETL/SSIS pipelines, data orchestration, file delivery and quality controls, follow SDLC/agile, mentor junior engineers, and partner with business stakeholders to capture requirements and ensure timely data delivery.

Design, build, and maintain scalable ETL/ELT data pipelines on Databricks and AWS S3; manage large geospatial and temporal datasets; productionize ML models; implement data validation, testing, and monitoring; optimize storage and processing; document workflows; collaborate cross-functionally.

Architect, build, and maintain scalable ELT data pipelines and analytics-ready data marts. Lead dbt modeling, ensure data quality, CI/CD, and documentation. Partner with stakeholders, mentor junior engineers, evaluate tooling (reverse ETL), and drive long-term data platform strategy.