Top Senior Data & Analytics Jobs in Lincoln, NE

The Lead Risk Analytics Data Scientist will develop risk pricing, design data models, automate workflows, and communicate insights to leadership. Responsibilities include owning the risk narrative, building analytics foundations, and utilizing AI to enhance efficiency.

Lead Data Engineer responsible for defining architecture, engineering standards, and best practices for the enterprise data platform, collaborating with distributed teams for modernization and scalability.

Lead design, implementation, and optimization of cloud-native data platforms (Snowflake, AWS, Azure) for managed-services clients. Translate business requirements into secure, scalable architectures, drive migrations and performance tuning, and provide hands-on technical leadership. Collaborate with cross-functional teams, mentor junior engineers, and contribute to best-practice frameworks, automation, and pre-sales efforts.

Leads and develops an analytics engineering team that builds data pipelines, analytical models, reporting, and scalable data infrastructure. Partners with Product, Engineering, Growth, and Operations to deliver reliable, actionable data. Establishes governance, quality, testing, documentation, and modeling standards while prioritizing analytics projects and communicating with stakeholders.

Designs and delivers a global analytics platform supporting reporting, visualizations, self-service analytics, and embedded data products. Responsibilities include data collection and processing, semantic modeling, warehouse and pipeline development, query optimization, governance, analytics enablement, and stakeholder collaboration. The role provides actionable insights from complex travel data and builds integrated analytics applications using cloud platforms and business intelligence tools.

Design, build, maintain, and optimize Spark and Scala ETL/ELT pipelines on AWS for terabyte- and petabyte-scale datasets. Work across S3, Snowflake, Kafka, and monitoring systems, resolve production data-quality and performance issues, write automated tests, participate in architecture and code reviews, and support on-call operations. Use generative AI to improve pipeline development, monitoring, and engineering productivity.

Lead enterprise data architecture across multiple SaaS products by unifying reporting platforms, data models, and analytics capabilities. Assess current systems, define target-state architecture, establish data governance and quality standards, separate analytical workloads from transactional systems, and develop phased migration roadmaps. Guide database, engineering, and BI teams on cloud platforms, integration, modeling, performance, security, and reliability. Enable trusted cross-product reporting, governed self-service analytics, and data foundations for AI and advanced analytics.

Design and build modern GCP lakehouse architectures, including Python and Spark pipelines, BigQuery ingestion, Kafka CDC, Delta Lake and Iceberg UniForm integrations, Delta Sharing endpoints, Snowflake and Databricks connectivity, data lineage, governed access controls, and semantic-layer alignment. Collaborate cross-functionally, troubleshoot full-stack issues, and deliver reusable data-sharing adapters and end-to-end data features.

Conduct research and provide strategic advice on HR technology, data governance, HCM systems, and AI’s impact on HR. Develop thought leadership, market analysis, presentations, and client recommendations for CHROs and HR technology leaders. Track vendors and emerging technologies, support sales and conferences, review peer content, improve research processes, and mentor junior analysts. The role requires executive presence, strong analytical and communication skills, and collaboration with global clients, including at least 6–8 hours monthly supporting Asia-Pacific organizations.

Leads audience research, advertising effectiveness, brand lift studies, and first-party audience analysis. Partners with Sales, Creative Strategy, Media Planning, and leadership to develop actionable insights, sales narratives, measurement strategies, presentations, and client recommendations. Manages research vendors, budgets, contracts, and methodologies while scaling research tools, dashboards, benchmarks, and best practices across the organization.