Top Senior Data & Analytics Jobs in Omaha, NE

The Senior Product Analyst will automate data collection, analyze product metrics, conduct A/B tests, and support product hypothesis generation.

Own end-to-end analysis of payments, chargebacks, and user lifecycle to identify drivers of net loss. Deep-dive into fraud cases, design and evaluate A/B experiments and risk-rule changes, build monitoring dashboards, partner with Risk, Payments, and Product to convert insights into mitigations, and run ad-hoc analyses of emerging loss trends.

Lead product analytics: build and maintain dbt models and Looker dashboards, define KPIs, analyze funnel and user journeys, design and evaluate A/B tests, size roadmap impact, and deliver partner and ad-hoc insights to inform product and executive decisions.

Design and implement BI solutions: build data models, automations, ETL/ELT workflows, dashboards, and ad-hoc analyses. Monitor and analyze RF/video performance using Dataminer/Qligent or similar tools. Collaborate with cross-functional teams, document requirements, and deliver actionable insights to optimize hybrid terrestrial and satellite video delivery.

Lead and oversee statistical programming activities, ensuring compliance with standards, managing deliverables, and implementing data strategies. Serve as an expert in statistical programming while guiding projects and improving processes.

Lead and manage institutional research activities: collect, analyze, validate, and report quantitative and qualitative data; support program/course reviews, accreditation, dashboards, and decision-making; supervise a team and manage ad-hoc and ongoing reporting and assessment projects.

Lead and manage institutional research activities: supervise staff, collect and analyze quantitative and qualitative data, produce reports and dashboards, support program/course reviews, accreditation, survey design, and provide data-driven recommendations to inform institutional planning and continuous improvement.

Design, build, and maintain scalable batch and streaming data pipelines, improve data modeling and governance, contribute to enterprise data architecture, and collaborate with cross-functional teams to ensure reliable, high-quality data for analytics and operational use.

Build, operate, and optimize large-scale data pipelines and warehouse schemas. Own Airflow orchestration, AWS-based data security (Lake Formation), automated data quality, and production incident resolution. Partner with stakeholders, mentor engineers, and drive DataOps best practices for performance, cost efficiency, and governance.

Provide operational support for enterprise analytics platforms (Databricks, Azure, SQL Server). Manage user access and governance, troubleshoot platform and data issues, document processes and data flows, enable users for self-service analytics, coordinate with engineering/infrastructure/security teams, and help optimize cloud costs and platform reliability.