Top Senior Analytics Jobs in Columbus, OH

The Director, Research - Power Markets is responsible for leading the QA/QC process for long-term forecasting, advising leadership, and ensuring market insights are effectively integrated into forecasts.

Design and run experiments, analyze large datasets, build production-ready data pipelines and analytical frameworks, and translate findings into actionable recommendations for marketing and product stakeholders. Communicate insights to senior leaders and use approved AI tools responsibly to accelerate analytics and automation.

Lead acquisitions analytics for Business Credit Card portfolio: set analytical roadmap, drive performance monitoring, forecasting, targeting, channel effectiveness, and test-and-learn programs. Manage and develop an analytics team, partner with cross-functional stakeholders, deliver executive-ready insights, and ensure governance, data quality, and regulatory-compliant reporting to inform acquisition strategy and risk-adjusted growth.

Perform advanced fraud risk analyses, develop and implement point-of-sale fraud strategies (rules, cutoffs, policies), identify key metrics, collaborate with cross-functional partners, and present findings to senior management to reduce fraud losses while minimizing customer impact.

Partner with product, engineering, BI, and business stakeholders to define requirements, own epics/stories, validate data, enable dashboards, document data assets, manage backlog, and drive adoption of curated finance data products and analytics.

Design, build, and maintain scalable dimensional data models, semantic layer, and production transformation pipelines. Collaborate with Data Engineering, Data Science, BI, DevOps, and Product to enable AI-ready data, ensure data quality and governance, support ML feature delivery, and coach analytics team members for robust, production-ready data products.

Lead and grow a team of analytics engineers while remaining hands-on with SQL, dbt, and Snowflake. Own roadmap and semantic layer development, build trusted, governed data products, partner with cross-functional stakeholders, champion data quality and observability, and hire and coach engineering talent to deliver scalable analytics and AI-ready foundations.

Lead end-to-end COGS and gross margin analysis for hardware and software products, model cost-saving scenarios, answer ambiguous executive questions, partner with data engineering/finance/product, build reliable datasets and analytical frameworks, and present recommendations to C-suite stakeholders.

Own SQL and dbt-based data modeling and data quality for clinical and claims datasets. Author and refactor dbt models/tests, triage DQ alerts, design monitors, optimize SQL performance, and collaborate with Data Engineering and stakeholders to ensure trusted production datasets.

Build and maintain governed Silver/Gold data models and semantic assets on Databricks. Translate stakeholder KPIs into testable, reusable transformations, apply enterprise modeling standards, enable domain self-service, ensure data sensitivity and governance, and review domain-contributed models for production readiness.

Build and scale the analytics foundation: own dbt projects and Snowflake warehouse, create trusted datasets and semantic layers for AI, build ELT pipelines from APIs, implement testing/observability and CI/CD, design reconciliation models, enable stakeholder self-service, and partner cross-functionally to drive data-driven decisions across GTM, Product, Finance, People, and Operations.

Design, build, and optimize modular dbt pipelines and data models; translate ambiguous product requirements into production-grade data assets; diagnose query performance and lower warehouse compute costs (including migration to Snowflake); enforce code hygiene, data security, testing, and peer review; mentor junior engineers and adopt AI-assisted development tools to speed test generation and legacy discovery.

Lead data management for marketing execution: design targeting and measurement strategies, build repeatable datasets, automate reporting, implement monitoring and quality checks, enable responsible AI-supported workflows, and quantify value from automation and audience improvements.

Lead design and build of ETL pipelines and Gold Layer tables in Databricks; develop semantic layers, data dictionaries, and AI-ready documentation; create dashboards and ad-hoc analyses to surface marketing and sales KPIs; partner with stakeholders to translate requirements into analytics-ready datasets and enable self-serve BI and agentic AI workflows.

Lead and scale a casino analytics team to drive data-informed strategy, experimentation, and reporting. Partner with product, data science, and operations to deliver executive insights, build analytical frameworks, and develop high-performing analysts to unlock growth and operational efficiency.

Lead a quant analytics team supporting AML/KYC controls by defining analytics strategy, delivering advanced data solutions, integrating AI, partnering with senior stakeholders, and improving governance and processes to drive measurable business outcomes and strengthen risk/compliance controls.

Lead strategic revenue cycle analyses to diagnose patient billing and collections performance, quantify ROI and value, build executive-ready recommendations and playbooks, partner cross-functionally, and mentor teammates to drive scalable, data-driven improvements.

Lead architecture, systems, and roadmap for Coinbase's Compliance Reporting Data Platform. Design and build production-grade data models, pipelines, quality frameworks, validations, and certification processes. Establish data contracts, monitoring, reconciliation, and audit-ready processes. Partner with engineering and compliance stakeholders, respond to regulatory exams, and mentor engineers while shaping standards and documentation.

Serve as a strategic analytics advisor designing and implementing Adobe Analytics and CJA solutions, conducting measurement workshops and audits, building dashboards, translating insights into action, training clients, and ensuring data security and privacy compliance.

Lead and execute analytics implementations (primarily Google Analytics/GTM or equivalents), consult on technical solutions, manage client interactions, QA project work in Asana, mentor junior consultants, and deliver insights and data visualizations for clients.

Lead and grow the Revenue Analytics team to deliver strategic sales and customer lifecycle insights. Build forecasting, pipeline, capacity, predictive models, and customer lifecycle analytics to improve revenue, retention, adoption, and expansion. Partner with GTM, Finance, Product, and Enterprise Data to create executive reporting, data governance, AI-enabled self-serve datasets, and integrated analytics that drive business outcomes and support a shift to consumption-based models.

Lead development of scalable, well-tested data products for Revenue: design dbt models, metrics, dashboards, governance, and AI-enable analytics. Drive technical direction, automation, quality, and mentor cross-functional partners to deliver trusted reporting and self-service analytics.

Partner with analytics teams to measure and improve marketing effectiveness; build AI-enabled detection and alerting solutions; translate business questions into analytical approaches; deliver actionable recommendations; own projects end-to-end and communicate insights to technical and non-technical stakeholders.

Lead analytics and machine-learning efforts to prevent point-of-sale fraud: analyze large datasets, design and implement fraud decisioning capabilities, partner with risk modeling and cross-functional teams, build KPI monitoring and automation, and communicate findings and project outcomes to senior leadership.

Lead global Customer Success analytics strategy, architect data and health/adoption models, oversee data pipelines, deliver executive-level storytelling, mentor an analytics team, and drive cross-functional KPI alignment and self-serve BI adoption.