Top Data & Analytics Jobs in Columbus, OH

Leads and mentors the Data Engineering team while remaining hands-on in designing, building, and maintaining scalable data pipelines, warehouses, integrations, and infrastructure. Establishes architecture, reliability, quality, and performance standards; guides technical delivery; and resolves complex issues. Partners with Finance and business stakeholders to translate data needs into practical solutions, sets technical direction, evaluates technologies, and communicates strategy, risks, and recommendations to leadership.

Leads and delivers complex data engineering projects, designing scalable data infrastructure, pipelines, warehouses, and data quality solutions. Recommends tools and architectures, optimizes data movement and performance, collaborates across technical teams, mentors engineers, and translates technical concepts for clients and nontechnical stakeholders. The role may also involve real-time processing, data modeling, transformation, security, and MLOps.

Monitor prediction markets and perpetual futures by tracking news, trader sentiment, venues, volumes, open interest, funding rates, and market share. Research protocols and users, build dashboards and datasets, identify product opportunities for Pyth, size opportunities with market and on-chain data, and collaborate with product, engineering, and business development teams. Write research reports, present recommendations to leadership, and evaluate launched initiatives against their original theses.

The Data Engineer will develop and maintain data pipelines, perform data extraction and transformation, and support data analytics across the enterprise.

Build and operate core data infrastructure for edge and hybrid environments, including ingestion, OT/IoT integrations, real-time streaming, data contracts, schemas, validation, lineage, observability, telemetry, and scalable query access. The role requires production ownership, reliability practices, customer integration, and collaboration across engineering and customer-facing teams. Candidates should have 6+ years of experience with production data, streaming, or integration systems and proficiency in Python and/or Go.

Build and own Snout’s data foundation, including PostgreSQL and dbt warehouses, ingestion pipelines, dimensional models, data quality, documentation, and reliable rebuilds. Reconcile data from veterinary practice-management systems, support LLM/AI tooling, translate stakeholder needs into technical plans, and establish the foundation for a future data team and analyst.

Develop and maintain BI reports and dashboards for the Mortgage Lending Division. Analyze large datasets, identify trends, validate reportable data, and support regulatory reporting such as HMDA and Fair Lending Analysis. Collaborate with business teams to gather requirements, automate reporting processes, document data definitions, and provide training on reporting tools. The role also supports data dictionaries, exception reporting, and operational improvements.

Develop and maintain Power BI, SSRS, SQL, recurring, and ad hoc reports for the Mortgage Lending Division. Analyze large datasets, validate regulatory reporting data, identify trends, automate reporting, and provide actionable insights. Collaborate with business units, support HMDA and fair lending reporting, document data dictionaries and procedures, and train users on reporting tools.

Administers and secures Oracle databases supporting DISA contract systems. Responsibilities include database installation, tuning, access management, outage response, disaster recovery, security patching, STIG compliance, scripting, release support, data modeling, legacy data migration, reconciliation, and system integration. The role also supports Appian data stores and coordinates database changes with DISA, hosting providers, ISSOs, and development teams.

Build and maintain SQL-based marketing analytics infrastructure, data models, dashboards, and reporting. Analyze marketing channels, affiliates, conversion funnels, cohorts, revenue, profitability, and partner performance. Translate complex findings into actionable recommendations for budget allocation and optimization. Ensure data quality, governance, documentation, and metric consistency while partnering cross-functionally with Marketing, Sales, Finance, Operations, Product, and Technology. The role is a hands-on individual contributor with potential responsibility for mentoring or managing one analyst.