Top Senior Data & Analytics Jobs in Columbus, OH

Conducts econometric, causal, statistical, and optimization analyses to improve operational efficiency, resource allocation, product performance, and business outcomes. The role defines efficiency measures, develops forecasting and optimization tools, partners with product and engineering teams on data instrumentation and implementation, and communicates findings to executives through clear reports.

Owns lead and hotel account data quality for a sales organization. Responsibilities include CRM audits, deduplication, enrichment, lead qualification, routing, SLA development, escalation resolution, data workflow documentation, vendor evaluation, SQL-based validation, and responsible use of AI for quality assurance and anomaly detection.

Owns a six-month analytics engineering engagement covering scalable Snowflake architecture, marketing and sales ingestion, finance automation with LLM agents, data reconciliation, and governed Tableau reporting. Builds production-ready pipelines, data models, quality monitoring, human-in-the-loop controls, and row- and column-level security. Delivers documentation and knowledge transfer while coordinating independently with finance and publishing stakeholders.

Administers and optimizes enterprise Oracle databases supporting global consular applications. Responsibilities include database operations, backup and recovery, replication, disaster recovery, performance tuning, security remediation, SQL and PL/SQL development, upgrades, migrations, monitoring, governance, documentation, and operational support for Oracle RAC, Data Guard, GoldenGate, and Oracle Linux environments.

Designs secure, scalable database and data-warehouse architectures; develops logical and physical data models, schemas, metadata, interfaces, and integration standards. Coordinates clustering, backup, recovery, load balancing, and availability strategies. Plans upgrades, recommends infrastructure and configuration improvements, and tests database structures and programs. Collaborates with architects, analysts, and project teams to align database solutions with business and technical requirements.

Own Mux’s end-to-end data platform, including ingestion, Snowflake administration, dbt transformations, orchestration, data quality, observability, and reverse ETL. Build semantic models supporting AI agents, establish governance and reliability standards, and partner with Engineering, Finance, Product, and GTM. Use AI coding tools to develop and maintain pipelines while validating output quality. The role requires strong SQL, Python, data modeling, cloud warehouse administration, and production data engineering experience.

Architect and build cloud-native data platforms, Snowflake pipelines, and datalakes for complex financial datasets. Lead legacy modernization, ETL/ELT, reconciliation, data quality, governance, and regulatory reporting initiatives. Drive technical product roadmaps, backlog prioritization, requirements, testing, cutovers, and releases. Coordinate Agile delivery through Jira and Confluence while aligning business, analytics, and engineering stakeholders across the full data and software lifecycle.

Maintains and monitors Azure Databricks data pipelines, troubleshoots data issues, collaborates with stakeholders on data requirements, documents processes, performs backups and disaster recovery drills, and supports data access and usage. The role requires extensive data engineering experience, Azure expertise, incident management knowledge, and strong problem-solving skills.

Designs, builds, and optimizes scalable Databricks-based data platforms, pipelines, integrations, and reusable engineering tools. The role focuses on platform performance, reliability, cost efficiency, security, governance, automation, and cloud infrastructure. It collaborates with engineering, analytics, security, and other stakeholders on architecture, technical planning, production support, and continuous improvement. The position requires hands-on data engineering experience with Databricks, cloud services, Python, SQL, Spark, infrastructure-as-code, and CI/CD practices.

Build and operate a governed Snowflake enterprise data warehouse, including ingestion, orchestration, dbt transformations, infrastructure as code, security controls, reliability checks, and CI/CD. Partner with data owners and BI teams during migration from legacy systems, ensure compliance for sensitive financial data, support reconciliation and incident resolution, and mentor engineers. The role requires hands-on ownership of pipelines and platform components across development and production environments.