Top Remote Analytics Jobs in Columbus, OH

Lead intake, grooming, and translation of reporting and analytics requests into actionable requirements. Drive stakeholder sessions, define data logic and metrics, prioritize requests, enable scalable reporting solutions, support delivery and UAT, mentor junior analysts, and improve intake processes to reduce one-off requests and promote reusable, self-service reporting.

Lead architecture and implementation of a large-scale analytics stack: design foundational pipelines, establish modeling/orchestration standards, integrate AI-assisted engineering tools, translate business questions into version-controlled data models, partner with stakeholders to define metrics and data contracts, migrate logic into maintainable pipelines, and optimize cloud warehouse performance for scale.

Lead the RevOps analytics function: manage an analytics team, own revenue reporting and forecasting, build measurement frameworks, enforce data governance, partner with BI/engineering, analyze funnel and retention metrics, and embed AI/automation to improve revenue predictability and GTM performance.

The Analytics Manager analyzes complex datasets, builds models, conducts scenario analyses, and provides strategic recommendations to guide decision-making for leadership.

The Senior Data Scientist will design and deploy time-series forecasting models, collaborate with stakeholders, and operationalize models in cloud environments, focusing on demand planning across various industries.

Lead the Payments Insights Analytics team, utilizing data and AI/ML for strategy execution, operational excellence, and stakeholder communication.

As a Data Analytics Engineer I, you'll bridge engineering and analytics, manage data pipelines, collaborate with stakeholders, and utilize modern open-source tech for data transformation and integration in structured finance.

The Director of Data Analytics will lead a team focused on data metrics, especially ARR, and oversee the execution of data initiatives across the organization.

The Senior Analytics Specialist develops processes for digital optimization and collaborates on A/B and multivariate testing through data insights.

The Data & Analytics Architect will design and implement analytics solutions, support client engagements, and lead data architecture projects, requiring extensive technical and consulting skills.

Lead analytics for law firm operations by writing advanced SQL and Python/R scripts, building executive dashboards, standardizing KPIs, analyzing case lifecycle metrics, identifying bottlenecks, and delivering actionable recommendations to drive capacity planning and process improvements.

Lead enterprise data, analytics, and AI transformation engagements as a strategic advisor and architect. Design cloud data platforms, warehouses/lakehouses, governance, integration, and BI solutions. Oversee delivery, establish standards, mentor teams, support presales, and drive measurable business outcomes across technologies like Oracle, Snowflake, Databricks, and Microsoft Fabric.

The Commercial Insurance Analytics Consultant leads analytical initiatives for commercial insurance, evaluates trends and risks, and makes recommendations to senior leadership while mentoring less experienced analysts.

Lead design and governance of enterprise ontologies, knowledge graph architecture, ingestion pipelines, and semantic layers. Enable semantic interoperability across data products, implement inference and reasoning, and establish standards, versioning, and data contracts to support scalable graph-driven analytics and AI augmentation.

The Senior Staff, Advanced Analytics will drive data strategy for the guest app, collaborating with stakeholders to improve conversion and create a multi-product experience through advanced data analysis and experimentation.

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

Design and maintain scalable data models and pipelines for analytics. Collaborate with teams to ensure reliable data supporting business insights.

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.

Design, build, and maintain dbt models, ELT pipelines, Looker explores and visualizations. Drive analytics strategy, data discoverability, observability, lineage, and security. Mentor engineers, collaborate cross-functionally, apply software engineering best practices, and integrate AI to scale data products and self-service BI.

Lead a team of data engineers and analysts, define the data engineering roadmap, oversee data infrastructure, ensure analytics delivery, and establish data governance standards.

The Senior Data Engineer will design, build, and support data environments, orchestrate pipelines, and implement data solutions for advanced analytics and machine learning, working closely with project teams.

The Analytics Engineer will design, build, and maintain scalable data models, collaborating with various teams to ensure data quality and robust reporting, mainly using dbt and SQL.

As a Director in the Data & Analytics team, you will lead client engagements, manage projects, develop strategies, create proposals, and mentor team members in data-driven initiatives.

Manage client data and analytics projects to design and implement data strategies, automation, visualizations, and predictive analytics. Translate data into actionable insights, lead cross-functional stakeholder engagement, scope and monitor budgets, and develop internal tools and templates. Apply finance/CPA knowledge where relevant and deliver client-facing presentations.

Manage client-facing data and analytics projects: define data strategy, build automation and ETL, develop visualizations and predictive analytics, drive transformation, manage scope/budget, and communicate findings to non-technical stakeholders while leveraging finance knowledge.