Top Data & Analytics Jobs in Dayton, OH

The role involves managing the data infrastructure at Convo, optimizing ETL/ELT processes, ensuring data quality, and collaborating with cross-functional teams on data projects.

The Senior Database Analyst will optimize a legacy SQL Server environment and design a modern platform using PostgreSQL and ClickHouse. Responsibilities include auditing server performance, designing database schemas, managing multi-tenant databases, and improving data ingestion methods.

The VP Global Grid Modeling will lead global data strategy, manage high-performing teams, ensure quality datasets, and engage with customers for insights in energy markets.

The Technical Enablement & Documentation Lead is responsible for developing and maintaining product documentation, creating a certification program, and collaborating with teams to improve the knowledge experience for Flosum's users.

As a Technical Solutions Manager, you'll onboard Enterprise customers onto Medallion's platform, focusing on data migration, integration, and automation for enhanced operational efficiency.

The Forward Deployed Data Engineer at ZoomInfo designs and delivers custom intelligence applications for strategic accounts, managing engagements end-to-end and collaborating with various teams to optimize data solutions.

The Senior Azure Data Platform Engineer manages Azure SQL databases and Data Factory operations, ensuring stability and performance in a complex environment. The role involves troubleshooting, automating processes, and engaging with clients while leading data load ownership initiatives and enhancing operational practices.

The Senior Finance Analytics Engineer designs dbt models, collaborates on metrics with finance teams, and enhances data for AI consumption while maintaining SOX compliance.

The Head of Data Science - Fraud Insights will lead research on fraud analytics, design predictive studies, manage a team, and communicate findings to influence strategy and policy.

Lead the technical roadmap and hands-on development of a scalable data platform, build analytics and data access solutions, ensure data quality/governance/observability, mentor and grow a team of data engineers, collaborate cross-functionally, and evaluate new big-data technologies.