Top Senior Data & Analytics Jobs in Lincoln, NE

The Senior Data Engineer will design, implement, and manage data pipelines for real-time analytics and ML training datasets, ensuring efficient data flow and quality in a hybrid environment.

Manage and secure enterprise data systems, govern statewide Power BI and AI readiness, maintain ETL/data integration, ensure data quality and compliance, support cloud migration, provide reporting and user support, and collaborate across agencies to enable data-driven decision-making.

Serve as an analytics partner to business units to define problems, scope projects, perform in-depth analysis, and deliver actionable insights. Lead technical execution with data engineers/scientists, build dashboards in Looker, apply statistical methods and A/B testing, and drive data literacy and process improvements across the company.

The Senior Data Analyst will deliver quantitative insights and strategic recommendations for Customer Success Commercial Operations. Responsibilities include analyzing user behavior, developing commercial models and forecasts, translating business needs into actionable analyses, communicating findings to executives, and building scalable, reproducible data assets using complex SQL and code-based workflows. The role also supports go-to-market strategy, retention initiatives, revenue planning, operational decisions, product roadmaps, and mentorship of junior analysts.

Lead administration, tuning, maintenance, security, backup and recovery, replication, and optimization of mission-critical databases supporting federal healthcare operations. Collaborate with developers, architects, analysts, security teams, operations, and vendors to ensure performance, scalability, data integrity, compliance, and secure PHI/PII handling. Support database migrations, upgrades, monitoring, auditing, and cross-system validation in cloud and integrated environments.

Manage enterprise metadata, business glossaries, data catalogs, and end-to-end data lineage for a federal case management modernization program. Configure governance and metadata platforms, integrate data sources and pipelines, establish standards and workflows, monitor metadata quality, support compliant data sharing, develop governance metrics, document procedures, and train data stewards and business users.

Own risk analytics initiatives that improve repayment rates, optimize collections costs, monitor trends and emerging risks, and evaluate external agency performance. Partner with stakeholders and vendors to develop actionable insights, support strategic decisions, and apply AI tools to automate reporting and improve collections outcomes. The role requires advanced SQL and experience in credit risk, fraud risk, collections, or related lending analytics.

Design, build, and maintain Power BI dashboards and data models using Snowflake and other cloud sources. Collaborate with data engineers/ETL teams, gather stakeholder requirements, implement DAX and Power Query transformations, ensure data governance and performance, and deliver analytics within Agile processes.

Design, build, and maintain Keboola-based data pipelines integrating multiple business systems to deliver accurate, scalable data for supply chain, logistics, and transportation analytics; collaborate with analysts and engineers; support documentation, governance, and pipeline automation.

Provide analytics, automation, and reporting to support compliance testing and monitoring. Collect, validate, reconcile, and analyze large data populations; develop repeatable testing routines, dashboards, and automated solutions; support sampling, exception analysis, reporting, and stakeholder requests to identify trends, risks, and remediation status.

Own the design, development, maintenance, and performance of Microsoft Fabric data pipelines, centralized semantic models, DAX measures, and documentation. Ensure data quality, consistency, and trusted marketing metrics across sources. Partner with data engineering and BI teams on deployments and production issues, investigate model discrepancies, perform analytical work, mentor apprentices, and contribute to applied AI initiatives.

Designs and scales cloud-native data platforms and ingestion pipelines using AWS, Databricks, and Spark. Builds APIs, file-upload mechanisms, ETL/ELT workflows, data transformations, and Lakehouse architecture supporting analytics, machine learning, and operational use cases. Provides operational support for legacy C# .NET ingestion processes, troubleshoots pipelines, and promotes data governance, security, reliability, and documentation in a multi-tenant SaaS environment.