Top Senior Data & Analytics Jobs in Columbus, OH

Build and own production data pipelines using Snowflake, Airflow, dbt, and Python; implement canonical data models; embed validation, quality checks, lineage, freshness, and alerting; support governed self-service data delivery; migrate workloads; partner with business consumers; participate in on-call and incident reviews; and mentor engineers while maintaining strong testing, CI/CD, documentation, and code-review practices.

Design, build, and lead a scalable, configuration-driven data integration platform. Improve performance, automate monitoring and deployments, provide escalated support, and collaborate across product and engineering to deliver reliable, secure data integrations.

Build and own workforce capacity, forecasting, and scenario models that guide hiring, coverage, account assignments, and organizational design. Maintain executive-ready dashboards and real-time visibility into workforce supply, demand, hiring pipelines, utilization, attrition risks, and workload distribution. Partner with HR, Finance, Revenue Operations, and business leaders to translate complex workforce data into actionable decisions and ensure consistent reporting and assumptions across systems.

Serves as a strategic analytics partner to Marketing, owning funnel, attribution, ROI, account scoring, experimentation, and pipeline analytics. Builds scalable datasets, dashboards, and quality controls while connecting customer, campaign, product usage, CRM, and revenue data. Delivers executive-ready reporting, QBR insights, and recommendations that improve conversion, marketing investment effectiveness, account-based marketing, product adoption, and pipeline growth.

Design and maintain scalable ETL/ELT pipelines, analytical dashboards, and reporting tools supporting clinical, research, and operational priorities. Write production-quality Python, R, and SQL; develop data architecture; partner with data scientists to operationalize analytics; integrate AI-enabled workflow improvements; establish engineering standards; and mentor junior team members.

Analyze performance marketing data, including traffic quality, unit economics, reporting, attribution, A/B testing, forecasting, and monetization. Build dashboards, anomaly alerts, and data models using BigQuery, dbt, and Tableau. Partner strategically with performance teams to identify growth opportunities, improve channel evaluation, and guide scaling decisions through SQL, Python, and statistical analysis.

Analyze complex financial and business data to guide planning, forecasting, investment decisions, and strategic growth. Partner with FP&A, Accounting, RevOps, Product, Marketing, Data Engineering, and analytics teams; automate recurring workflows; build self-service dashboards and data assets; improve analytics infrastructure; and communicate actionable insights to technical and non-technical stakeholders.

Investigate offline and direct mail channel performance, attribution, vendor data, and measurement methodologies. Build response, lift, allocation, and regression models; manage direct mail testing data pulls and mail merges; create KPI dashboards; and communicate actionable recommendations to marketing stakeholders. The role requires proactively identifying opportunities, validating data and vendor claims, and translating complex analytical findings into business decisions.

Design, develop, and maintain scalable data pipelines and models in Databricks and Microsoft Azure. Perform data ingestion, transformation, integration, migration, and modernization across enterprise data sources. Monitor and optimize pipeline performance, reliability, and data quality. Translate business requirements into technical solutions, collaborate with architects and engineers, follow security and governance standards, participate in code reviews, and document data platforms and processes.

Lead advanced product analytics for a consumer product team by analyzing complex data, designing experiments, applying statistical and causal inference methods, developing metrics and reporting tools, and delivering actionable recommendations to cross-functional stakeholders and senior leadership. The role requires strong Python or R and SQL skills, product analytics experience, AI-assisted analytics proficiency, and the ability to influence product strategy.