Top Senior Analysis Reporting Jobs in Columbus, OH

Lead enterprise efforts to define and enforce metadata, semantic, and data-readiness standards. Profile domains, surface and remediate quality/definition gaps, convert fixes into scalable patterns, build prototypes for analytics and AI use cases, and report readiness scorecards and KPIs to senior stakeholders.

Leads a marketing analytics team that evaluates campaign performance, develops statistical models and experiments, delivers in-flight and post-campaign insights, and advises stakeholders on optimization strategies. Manages analytics initiatives, resources, data-driven research, and talent development while identifying improvements to data systems and business processes. The role requires translating complex findings into actionable recommendations and overseeing analysis of large structured and unstructured datasets.

Own the roadmap and backlog for insurance pricing tools, gather requirements, coordinate delivery, and develop scripts and processes across rating programs. Analyze premium mismatches, maintain SAS pricing tools, conduct pricing analyses and quality checks using Python, SAS, Excel, and related technologies. Collaborate with cross-functional teams, resolve defects, present findings, train colleagues, and support successful pricing model rollouts.

Analyze complex datasets, develop data models, dashboards, and visualizations, and conduct predictive and statistical analysis for client decision-making. Collaborate with clients to deliver tailored analytics solutions, maintain data integrity, communicate insights through reports and presentations, and support data-driven operational strategies using SAS, Power BI, Tableau, and SQL tools.

Lead revenue analytics by developing business cases, dashboards, KPI frameworks, experiments, and automated analytics solutions. Partner with client success, marketplace, merchants, and cross-functional teams to evaluate performance, guide go-to-market decisions, quantify product value, and scale data-driven execution. Serve as the analytics subject matter expert while translating structured and unstructured data into actionable recommendations and compelling stakeholder narratives.

Lead customer listening and Voice of Customer programs, including NPS, CSAT, CES, surveys, and interviews. Analyze qualitative and quantitative customer data to identify drivers, trends, pain points, and opportunities. Present insights to executives and cross-functional teams, facilitate action planning, and drive systemic customer experience improvements. Evaluate AI tools for sentiment analysis, theme extraction, and customer research while helping build a data-driven, customer-centric culture.

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.

Serves as an enterprise architecture subject matter expert within a DoD environment. Supports DoD Business Enterprise Architecture initiatives, business process alignment, enterprise strategy, stakeholder analysis, requirements documentation, reporting, and project activities using MEGA HOPEX and Microsoft Office tools.

Designs and develops complex cloud-native data solutions, scalable batch and real-time pipelines, APIs, data models, and enterprise data platforms. Implements data governance, quality, lineage, security, observability, CI/CD, automated testing, and operational reporting capabilities. Collaborates with architects, product owners, analysts, governance, security, and platform teams on technology strategy and cloud modernization. Mentors engineers, leads technical design discussions, presents recommendations, and supports AI-ready data products and emerging development tools.

Leads and develops a Data Platform engineering team while owning delivery of complex data initiatives. The role establishes delivery discipline, assesses technical and implementation risks, partners cross-functionally, and drives production reliability, data quality, observability, incident management, and on-call readiness. It also guides the evolution of scalable data ingestion, transformation, lakehouse, cloud architecture, governance, and reusable platform capabilities.