Top Remote Data & Analytics Jobs in Omaha, NE

Collects and researches equipment cost data for quarterly product updates, performs data analysis, and supports ownership and operating cost calculations. Maintains reports and dashboards, responds to data requests, audits processes, improves documentation, and partners with engineering and data quality teams to streamline workflows and ensure reliable, high-quality data.

Manages enterprise data governance and privacy compliance programs, including GDPR and CCPA adherence, data subject rights processes, governance controls, privacy documentation, compliance metrics, and continuous improvement. Coordinates with business, legal, compliance, security, and technology stakeholders; maintains process documentation; monitors risks and response timelines; supports automation, reporting, awareness training, and audit readiness across global operating units.

Lead marketing analytics: own reporting frameworks, dashboards, and data quality; monitor KPIs; run A/B tests and experiments; build attribution, forecasting, segmentation, and budget-allocation models; automate workflows and drive continuous measurement improvements.

Own advanced analytics for Airbnb product teams, using large datasets, statistical modeling, experimentation, causal methods, forecasting, and metrics development to guide product and business decisions. Build reporting and analytical tools, collaborate cross-functionally, automate repetitive work with AI, and present actionable insights to senior stakeholders. The role supports Guest App or API initiatives, including promotions and marketplace optimization.

Conduct product analytics for MoneyGram’s digital money transfer products. Analyze customer behavior, product performance, experiments, feature releases, and cohort metrics to guide product strategy and growth. Build insights and reporting, collaborate with Product, Engineering, Marketing, Finance, and international teams, and improve analytics platforms, automation, and visualization processes. Translate complex findings into actionable recommendations for technical and non-technical stakeholders.

Leads enterprise Business Intelligence strategy, architecture, governance, security, and self-service analytics adoption. Architects solutions across Power BI, Tableau, SAP BusinessObjects, and Databricks; establishes semantic models, data standards, DevOps, CI/CD, and reporting performance practices. Partners with executives and technical teams to modernize analytics platforms, reduce manual reporting, and enable AI-ready decision-making. Mentors BI professionals and drives enterprise data literacy and analytics transformation.

Designs, develops, and maintains scalable ETL/ELT pipelines and data platform capabilities for high-volume healthcare data. Responsibilities include complex SQL development, Airflow orchestration, dbt transformations, data quality frameworks, pipeline monitoring, troubleshooting, performance optimization, and documentation. The role collaborates with Data Operations, Product, Analytics, Data Science, and clinical teams to deliver reliable data for analytics, reporting, product features, and AI/ML initiatives.

Analyze market and live data to set dynamic ticket prices using Uptick, build strategic pricing recommendations, communicate with clients about strategy and performance, and collaborate with BI and engineering to improve product performance.

Analyze healthcare operations data to identify bottlenecks, model advocate capacity and supply-demand, build scalable Hex dashboards, refine SQL and dbt data transformations, and translate complex findings into operational recommendations. The role partners with Operations, Engineering, Product, Marketing, and Finance to improve workflows, reporting, resource allocation, and business outcomes across the patient and advocate journey.

Leads LTK’s Data Engineering team, owning technical direction, architecture, pipelines, data products, governance, quality, reliability, and platform modernization. Manages and develops engineers, drives roadmaps and complex initiatives, partners with cross-functional stakeholders, and balances scalability, security, performance, cost, and business priorities. Establishes engineering standards, improves operational practices, manages risks and dependencies, and builds trusted, reusable data capabilities supporting analytics, reporting, products, and AI use cases.