Top Senior Analytics Jobs in Omaha, NE

Architect, build, and maintain scalable ELT data pipelines and analytics-ready data marts. Lead dbt modeling, ensure data quality, CI/CD, and documentation. Partner with stakeholders, mentor junior engineers, evaluate tooling (reverse ETL), and drive long-term data platform strategy.

Lead development of provider data products and ML-based decision support, manage provider data quality, collaborate across teams to build provider-facing analytics, and establish MLOps standards and governance.

Manage a team of ~7 data scientists to deliver experimental design, product analytics, and actionable insights. Partner with product, engineering, and research to build measurement, data products, and reporting; ensure data accuracy, timely delivery, and best practices.

Lead a team of ~7 data scientists to deliver accurate product analytics and experimentation insights. Manage people, mentor staff, partner with product and engineering to build accessible data products, and improve measurement, reporting, and experiment practices to inform product decisions.

Lead a team of ~7 data scientists to deliver timely, accurate product analytics and experimentation insights. Manage people, partner with product and engineering, improve measurement and data products, and ensure rigorous large-scale analysis to inform product decisions.

Lead a Product Analytics data science team to deliver timely, accurate insights for product decisions. Manage ~7 data scientists, mentor teammates, partner with product and engineering, develop experimentation and measurement best practices, and build accessible data products and reports.

Lead a team of ~7 data scientists to deliver timely, accurate product analytics and experiments; support product teams, build accessible data products, mentor staff, and improve measurement and reporting practices.

Lead a team of ~7 data scientists to deliver timely, accurate product analytics: guide experimentation, partner with product and engineering, build accessible data products, mentor staff, and improve measurement and reporting practices to inform product decisions.

Lead a team of product analytics data scientists to deliver timely, accurate quantitative insights for product strategy, experimentation, and measurement. Manage people, mentor staff, partner with product and engineering to build accessible data products, and improve experimentation and reporting practices to inform decisions across the Foundation and movement.

Lead the data organization to define data strategy, build scalable cloud-native pipelines, enable self-service analytics, drive AI/ML initiatives, and manage a multidisciplinary team to inform product, clinical, and business decisions.

Analyze and forecast financial data for strategic decisions, lead a team, ensure data integrity, and advise executives. Requires extensive actuarial experience and modeling proficiency.

The Staff Analytics Engineer will establish a data foundation aiding various teams at Kit by refining reporting models, ensuring consistent metric definitions, and enhancing stakeholder self-service capabilities.

The Manufacturing Analytics Engineer will manage the analytics pipeline, transforming operational data from OT systems into actionable insights for decision-making, while collaborating with Engineering, IT, and Operations.

Hands-on technical lead designing and delivering secure, scalable Azure analytics solutions. Lead solution architecture, guide implementation, mentor engineers, advise on Power BI architecture and governance, and support delivery, consulting, and reuseable asset creation.

As an Analytics Manager, you'll build dashboards, provide strategic insights, and create a standard data language across teams to enable data-informed decisions.

The Director of Analytics will lead the analytics strategy and operations for a top-grossing mobile app, driving data-driven decision-making and insights.

Lead a team of ~7 data scientists to deliver timely, accurate product analytics and experimentation insights; manage people, mentor staff, partner with product and engineering, and build accessible data products and measurement practices.

Lead analytics engineering: architect, build, and maintain scalable data pipelines and dbt models; ensure data is research-ready; partner with Engineering, Data Science, and Commercial teams; implement product analytics and optimize data workflows for scale.

The Senior Analytics Engineer will design and build scalable data pipelines using dbt and Snowflake, providing technical guidance, conducting audits, and mentoring junior engineers while leveraging AI tools to enhance productivity.

Drive marketing performance measurement and attribution by building dashboards, standardizing campaign tracking across HubSpot and Salesforce, running funnel and cohort analyses, ensuring data integrity, and delivering executive reporting to connect marketing activity to pipeline and revenue.

Design and maintain ELT pipelines, build dimensional (star/snowflake) analytics models, implement data transformations and quality checks, document data lineage and governance, partner with BI users, and mentor junior engineers to deliver performant analytics for business reporting and KPIs.

The role involves executing advanced analytics, applying statistical techniques, designing reporting capabilities, and translating business needs into analytical solutions while ensuring data integrity and communicating insights effectively.

Lead design and reporting of end-to-end labeling KPIs and dashboards, ensure data integrity and governance, support compliance forums, audits, and digital enablement, and provide analytic insights to drive process improvements and CAPAs while offering cross-trained operational backup.

Lead and scale Arcadia's Data & Analytics engineering org, define multi-year data platform and unified data model, optimize Snowflake architecture, enable cross-functional data usage, set governance standards, and drive technical execution across ingestion, transformation, and analytics.

Partner with cross-functional teams to analyze large healthcare datasets, build models and dashboards, and surface insights to reduce medical waste. Scope, run, and present analyses, monitor performance metrics, and support initiatives to improve financial and utilization outcomes.