Top Senior Data & Analytics Jobs in Columbus, OH

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 Principal Data Platform Engineer leads the architecture and delivery of data platforms using Databricks, focusing on data pipeline design and client relationships.

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.

The Senior DataOps Engineer oversees data management and storage systems, ensuring timely access to secure and accurate data, improving data workflow automation, and collaborating with teams to meet business needs.

Design, implement, and manage scalable multi-cloud data platforms and ELT pipelines (AWS/Azure). Build and optimize Snowflake data models, dbt transformations, and Airflow orchestration. Define data engineering standards, enforce governance and quality, collaborate cross-functionally, and mentor global/offshore teams.

The Senior Data Engineer will design and maintain data pipelines, write production-grade Python and SQL code, and support AI initiatives while collaborating across teams.

Own and improve dbt models, ELT pipelines, and warehouse architecture (Snowflake or equivalent) to deliver reliable executive KPIs and metric governance. Build data spine for Finance/Operations, enforce data quality/testing, partner cross-functionally with Engineering, FP&A, and Accounting, and influence analytics engineering standards, tooling, and roadmap.

The Sr. Data Engineer will build and maintain data pipelines, develop ETL processes, and optimize data infrastructure, collaborating across teams to ensure data reliability and performance.

Lead fraud detection and mitigation for a conversational BNPL product. Define fraud typologies, own repayment and chargeback analytics, manage fraud KPIs, design operational policies, collaborate on ML models and decisioning workflows, and coordinate SARs and compliance reporting.

Design and maintain enterprise data architecture and lakehouse solutions. Lead data migration, integration, governance, and BI enablement. Define strategy, implement pipelines, ensure data quality, security, and provide technical leadership and mentoring.