Top Data & Analytics Jobs in Columbus, OH

Provides operational support for fundraising and business systems through recurring data maintenance, duplicate remediation, bulk uploads, record merges, data quality initiatives, and user support. Responds to ServiceNow requests, creates documentation and training resources, tests system enhancements, and recommends process improvements. The role also supports data validation, reconciliation, reporting, and automation using programming and AI while escalating complex issues appropriately.

Lead growth analytics for Zendesk’s product-led growth motion, guiding a team of analysts and partnering with Product, Engineering, Sales, Marketing, and Data teams. Define funnel metrics, measurement plans, experiments, and instrumentation across acquisition, activation, conversion, retention, and expansion. Analyze customer behavior using segmentation, cohorts, funnels, experimentation, and statistical methods. Improve self-service reporting, dashboards, metric definitions, reusable datasets, and analytics capabilities while coaching analysts and influencing senior stakeholders.

Analyzes and prepares customer data for successful migration to ServiceTitan. Works directly with customers to understand source databases, identify data issues, interpret database structures, and map data to ServiceTitan’s model. Writes queries to correct, reformat, and transfer data while coordinating with engineers, implementation teams, and onboarders. Manages shifting priorities, deadlines, project updates, and cross-functional communication to ensure accurate, timely data delivery.

Build and maintain automated ETL/ELT data pipelines, improve data quality and monitoring, optimize Postgres and BigQuery schemas and queries, develop internal tooling, support data governance (HIPAA), participate in on-call rotation, and collaborate across engineering teams to scale data systems.

Design, build, and maintain scalable big data pipelines, architectures, distributed databases, and storage systems. Use Python and frameworks such as Hadoop, Spark, or Flink for data integration, transformation, and processing. Ensure system performance, availability, security, governance, and data quality while collaborating with cross-functional teams and documenting technical solutions.

Design and implement scalable, secure enterprise data solutions on Databricks and Azure using Delta Lake and Medallion Architecture. Lead and mentor data engineering teams; define integration patterns, governance, security (Unity Catalog, RBAC, masking, row/column-level security), and CI/CD/IaC practices. Ensure data quality, lineage, monitoring, and optimize Spark/Delta workloads for performance and cost while aligning solutions with business needs.

Deliver recurring and ad hoc commercial analytics, client reporting, performance-guarantee calculations, dashboards, and value measurement. Define and govern metrics, validate data, improve documentation and workflows, and automate repeatable processes. Partner with Client Success, Commercial, Finance, Product, and Analytics teams to translate complex questions into actionable insights, client-ready narratives, and recommendations that strengthen client relationships and inform business strategy.

Own the customer experience data foundation by designing dbt models, pipelines, data quality tests, documentation, semantic layers, dashboards, and self-service datasets. Integrate support, KYC, withdrawal, CRM, workforce management, and market resolution data into the cloud warehouse. Identify recurring support issues, communicate findings to stakeholders, and partner with data engineering on infrastructure and governance.

Analyzes business, customer, sales, pricing, rebate, and profitability data using data warehouses, databases, spreadsheets, statistical tools, and presentation software. Develops reports, recommendations, sales plans, contracts, and customer profiles; troubleshoots discrepancies, documents procedures, supports strategic initiatives, and participates in special projects. Requires independent problem-solving, strong communication, organization, and attention to detail.

Design and implement end-to-end cloud data solutions, lead or mentor engineering teams, ensure performance, security, and scalability, integrate diverse data sources, write and optimize SQL, program in Java/Python/Scala, produce presentations and detailed solution documentation, and engage directly with clients to deliver production-ready systems.