Top Senior Analysis Reporting Jobs in Columbus, OH

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

Lead and grow a data engineering team that builds and operates ingestion, transformation, orchestration, and serving layers and a self-serve analytics substrate. Own data quality, observability, cost efficiency, SLAs, and engineering practices while partnering with Data Science, BIE, Analytics, and Product to deliver reliable, reusable data products.

Develops and maintains Salesforce and Tableau analytics dashboards, performs complex data analysis and forecasting, and delivers insights for sales, product, finance, and marketing decisions. Translates business needs into technical requirements, manages platform enhancements with developers, validates data quality, models sales goals, identifies optimization opportunities, and presents strategic recommendations. Provides user training and supports analytics across Treasury Solutions.

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.

Own Silver and Gold medallion-layer modeling and semantic layer governance using dbt and SQL. Improve warehouse performance and cost, standardize BI tooling, collaborate with stakeholders to produce trusted metrics, troubleshoot data quality, and evolve analytics platform.

Partner with People teams to turn HR and org data into actionable insights. Build complex queries, statistical analyses, dashboards, and a centralized people data repository. Drive data governance, data quality, and stakeholder communication to support hiring, retention, productivity, and workforce decision-making.

Owner of the analytics foundation: design and maintain BigQuery/dbt models, pipelines, and Looker Studio reporting; build AI workflows and web apps on GCP; integrate marketing, e-commerce, and fulfillment data; ensure GCP ops, security, and reliability; use AI-assisted tools daily and collaborate with business and client stakeholders.

Designs and leads enterprise-scale data platforms, pipelines, lakehouses, warehouses, and streaming solutions. Defines data modeling, governance, security, and architecture standards while optimizing performance, scalability, reliability, and cost. The role collaborates with data engineering, analytics, machine learning, and business teams; mentors engineers; and establishes data platform best practices using Snowflake, Databricks, BigQuery, Redshift, Spark, Kafka, and cloud technologies.

Build and operate large-scale data infrastructure supporting AI training and evaluation pipelines. Responsibilities include data ingestion, transformation, quality assurance, lineage, and high-throughput delivery across diverse modalities. The role requires operating petabyte-scale storage and pipeline systems, applying strong software engineering practices, and optimizing data infrastructure for model quality and training efficiency. Preferred experience includes multimodal datasets, data quality tooling, privacy-preserving systems, open-source data infrastructure, and frontier model training pipelines.

Leads Enterprise Intelligence operations and governance, including analytics demand intake, prioritization, KPI catalog management, documentation standards, service expectations, controls, and delivery reporting. Manages governance forums, coordinates with business and technology partners, oversees analytics operations and capacity planning, and develops the EI operations team. Drives standardization, data stewardship, automation, accountability, and continuous improvement across the analytics delivery lifecycle.

Leads the design, development, and support of customer-focused data and business intelligence solutions, including data models, reports, dashboards, and self-service analytics tools. Partners with stakeholders and IT, promotes data governance and quality standards, supports data lake design, and develops scalable analytics products. Provides technical leadership, mentors team members, manages project deliverables, and applies data science concepts to generate predictive and prescriptive insights.