Top Senior Data & Analytics Jobs in Omaha, NE

Design, build, and own scalable data pipelines and warehousing architecture from ingestion through analytics and incentive payouts. Lead data platform architecture, dbt modeling, data quality, lineage, observability, performance, and cost management. Partner with engineers and analysts on telematics accuracy and driver incentive reporting. Use AI coding tools daily to develop and review pipelines, SQL, and data models.

Manages a data engineering team responsible for scalable, reliable data pipelines, infrastructure, and analytics tooling. Provides mentorship, performance management, and technical leadership while coordinating with product, analytics, finance, compliance, engineering, and regulators. Prioritizes cross-functional projects, supports data architecture and governance, ensures performance and reliability, and operates within strict regulatory and audit requirements.

Own the company’s data function by designing and optimizing Snowflake architecture, pipelines, integrations, data models, BI dashboards, metric definitions, governance, and data-quality standards. Partner with leadership and cross-functional teams to translate operational needs into trusted data products. Automate recurring reporting, troubleshoot ingestion issues, perform ad hoc analysis, and establish scalable analytics practices as the first dedicated analytics hire.

Owns enterprise workforce intelligence and employee listening strategy. Analyzes employee sentiment, workforce trends, talent, skills, organizational effectiveness, and work data to identify risks and opportunities. Develops dashboards, analytical methodologies, AI-enabled insight products, and self-service resources. Partners with HR leaders and data owners to translate complex quantitative and qualitative findings into actionable recommendations for workforce planning, succession, organization design, automation, and capability decisions.

Own sales analytics and forecasting as the trusted source of truth for sales leadership. Build predictive and machine learning models to assess deal close likelihood and slippage, maintain Salesforce and Tableau dashboards, improve data governance, and translate complex business questions into actionable GTM recommendations. Partner with Sales, Marketing, and Finance on reporting standards, strategic planning, QBRs, and executive insights.

Lead enterprise data architecture across AWS and Databricks Lakehouse: design scalable data models, ensure governance and data quality, mentor teams, evaluate and adopt data technologies (Delta Lake, Unity Catalog), oversee large projects and deliverables, and incorporate generative AI to accelerate data modeling and delivery.

Support Numerator clients through ad hoc analyses, consumer-panel insights, platform guidance, reporting, and recommendations that improve sales and marketing effectiveness. The role requires understanding syndicated and household panel data, business intelligence platforms, client relationship management, presentations, and data-driven problem solving. It also involves developing expertise in Numerator solutions and sharing consumer behavior findings through industry content, forums, whitepapers, and blogs.

Designs and implements Google Cloud database migration strategies, including architecture, data models, database objects, pipelines, orchestration, security, and data transfers. Advises customers on Cloud Spanner and Cloud SQL implementations, translates SQL and stored procedures, configures Google Cloud components, performs performance testing, resolves performance issues, and delivers technical workshops and architectural recommendations.

Develop and optimize marketing reports, monitor product and advertising campaign KPIs, integrate and visualize data, investigate performance deviations, analyze campaigns, and recommend improvements. The role requires strong Google Sheets skills, basic SQL, A/B testing, mathematical statistics, and English proficiency. Python, web analytics, Google Ads, and BI experience are preferred. This is a fully remote position.

Leads the design and implementation of AI-powered data pipelines, enterprise feature stores, analytics-ready repositories, and self-service data access layers. Builds automated feature engineering, data quality, observability, and governance capabilities for large-scale data science workloads. Partners with Enterprise IT, establishes data availability and quality SLAs, integrates structured and unstructured data sources, and leads teams delivering scalable analytics infrastructure.