Top Senior Data & Analytics Jobs in Columbus, OH

The Business Analyst will document user stories, collaborate with product owners, lead scrum sessions, perform system analysis, and support implementation efforts. They will provide training and update process documentation.

The Lead Risk Analytics Data Scientist will develop risk pricing, design data models, automate workflows, and communicate insights to leadership. Responsibilities include owning the risk narrative, building analytics foundations, and utilizing AI to enhance efficiency.

The Senior BI Analyst will gather requirements, develop scalable reporting solutions, and create dashboards using Power BI, ensuring data-driven decisions across the organization.

The Lead Partner Success Implementation Engineer drives partner success through strategic implementation, technical guidance, and collaboration, ensuring optimal platform usage and partner enablement.

Lead the architecture and design of an operational data platform, focusing on data unification, streaming integrations, governance, and mentoring teams while ensuring HIPAA compliance.

Design, optimize, and govern Komodo's Snowflake-centered data infrastructure, integrate with Databricks and adjacent stores, reduce costs and improve query performance, implement monitoring/automation, establish metadata and RBAC governance, and coach teams on best practices to meet compliance and performance SLAs.

The Senior Business Systems Analyst collaborates with stakeholders to translate business requirements into technical specifications, manages requirements throughout the project lifecycle, and ensures alignment with organizational goals while enhancing efficiency in processes.

As a Senior Data Engineer, you'll design and maintain scalable data pipelines, optimize workflows with dbt and Snowflake, and mentor teams across functions to enhance data reliability and performance.

Embed on the Video product team to build reusable data models, transformation layers, and pipelines (Snowflake, dbt/SQLMesh). Deliver production-ready data products (APIs, dashboards), instrumentation, experimentation infrastructure, and predictive/causal models to inform product, retention, pricing, and growth decisions.

Design and maintain a dbt-based business data layer for GTM metrics, centralize and reconcile multi-source data, implement automated data quality and reconciliation checks, align and document metrics with stakeholders, mentor analytics team, and lead root-cause investigations into complex data anomalies.