Top Senior Data & Analytics Jobs in Columbus, OH

The Senior Data Analyst will deliver quantitative insights and strategic recommendations for Customer Success Commercial Operations. Responsibilities include analyzing user behavior, developing commercial models and forecasts, translating business needs into actionable analyses, communicating findings to executives, and building scalable, reproducible data assets using complex SQL and code-based workflows. The role also supports go-to-market strategy, retention initiatives, revenue planning, operational decisions, product roadmaps, and mentorship of junior analysts.

Own risk analytics initiatives that improve repayment rates, optimize collections costs, monitor trends and emerging risks, and evaluate external agency performance. Partner with stakeholders and vendors to develop actionable insights, support strategic decisions, and apply AI tools to automate reporting and improve collections outcomes. The role requires advanced SQL and experience in credit risk, fraud risk, collections, or related lending analytics.

Provide analytics, automation, and reporting to support compliance testing and monitoring. Collect, validate, reconcile, and analyze large data populations; develop repeatable testing routines, dashboards, and automated solutions; support sampling, exception analysis, reporting, and stakeholder requests to identify trends, risks, and remediation status.

Own the design, development, maintenance, and performance of Microsoft Fabric data pipelines, centralized semantic models, DAX measures, and documentation. Ensure data quality, consistency, and trusted marketing metrics across sources. Partner with data engineering and BI teams on deployments and production issues, investigate model discrepancies, perform analytical work, mentor apprentices, and contribute to applied AI initiatives.

Designs and scales cloud-native data platforms and ingestion pipelines using AWS, Databricks, and Spark. Builds APIs, file-upload mechanisms, ETL/ELT workflows, data transformations, and Lakehouse architecture supporting analytics, machine learning, and operational use cases. Provides operational support for legacy C# .NET ingestion processes, troubleshoots pipelines, and promotes data governance, security, reliability, and documentation in a multi-tenant SaaS environment.

Designs, implements, operates, and optimizes database and data-management systems supporting classified intelligence missions. Responsibilities include data architecture, SQL and NoSQL database administration, query and throughput optimization, ETL pipeline optimization, performance monitoring, capacity planning, documentation, backup and restore, audit logging, and continuity planning. The role requires onsite work in the Washington, DC region, U.S. citizenship, and a final Top Secret clearance with SCI eligibility or access.

Own high-impact data products, ingestion pipelines, and granular data feeds end to end. Analyze massive datasets to answer client and stakeholder questions, ensure data accuracy, and derive actionable insights. Improve products and pipelines by developing methodologies, scaling third-party datasets, and creating data modules. The role requires clear communication, attention to detail, methodical problem solving, prioritization, experimentation, and strong ownership. Training includes SQL, PySpark, Python fundamentals, and internal tools.

Analyzes retailer, marketplace, syndicated, and household panel data to identify customer and category growth opportunities for Target’s Self Care business. Develops assortment, pricing, promotion, competitive, seasonal, and omni-channel recommendations for online and in-store growth. Presents insights, collaborates with customer development managers and internal partners, supports go-to-market initiatives, and improves analytical best practices and accuracy.

Manages the enterprise Data Governance and Analytics Center of Excellence, leading staff, governance initiatives, analytics strategy, maturity modeling, budgets, vendors, and planning. Coordinates data quality, stewardship, privacy, acquisition, and release programs while partnering with data scientists, engineers, technology governance teams, and business stakeholders. Develops executive presentations, aligns enterprise definitions and algorithms, establishes standards, and ensures analytics projects and business cases meet organizational objectives and deadlines.

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.