Top Analysis Reporting Jobs in Lincoln, NE

Conducts public health data validation, management, quality assurance, geospatial and molecular HIV analysis using SAS and R. Responsibilities include developing codebooks and data management plans, identifying discrepancies, performing duplicate and comparison procedures, generating reports, interpreting results, disseminating data, and collaborating with analysts, federal partners, jurisdictions, and community-based organizations.

Develops and supports claims-related data pipelines, curated datasets, reporting layers, dashboards, and analytics assets. Partners with stakeholders to research data, clarify ambiguous requests, and design scalable solutions. Writes complex SQL, builds Power BI reports, and develops Databricks pipelines, transformations, workflows, and automation. Supports migration to Databricks and contributes to AI/ML and generative AI initiatives involving healthcare data.

Build, test, maintain, and enhance Workday financial reports, dashboards, and reporting solutions. Gather requirements, validate and reconcile data, document report logic, troubleshoot performance issues, support Workday implementations and releases, and train end users. Partner with Finance, HR, Supply Chain, and other stakeholders to ensure accurate, compliant, and actionable reporting across a nonprofit healthcare system.

Leads the design and delivery of scalable data pipelines, dbt models, orchestration workflows, and analytical data models. Establishes data architecture, engineering standards, quality frameworks, governance, observability, and self-service analytics practices. Partners with stakeholders, leads technical reviews, mentors engineers, supports hiring, oversees analyses, and participates in incident response and continuous improvement.

Lead reporting and application-integration architecture across Power BI, PBIRS, Power BI Embedded, and Crystal Reports. Build and optimize paginated reports, SQL queries, and stored procedures; troubleshoot app-to-report integrations; support Python data tooling; automate report delivery; implement monitoring, quality checks, anomaly detection, SLAs, and performance benchmarking.

Design and maintain scalable dbt data models and ELT pipelines in Snowflake. Architect data foundations to power AI and LLM-enabled workflows, operationalize core SaaS metrics, and build reusable data assets for analytics and activation. Partner cross-functionally to embed analytics into workflows and ensure scalable, secure architecture.

Design and maintain scalable dbt data models and ELT pipelines in Snowflake; architect reusable data assets, governance, and SaaS metrics; build AI-enabled data foundations and LLM workflows; integrate analytics into operational tools and partner cross-functionally to productionize AI-driven use cases.

Develop and maintain scalable Snowflake data warehouses, ETL/ELT pipelines, data models, and integrations across cloud and on-premises sources. Optimize queries, clustering, partitioning, and warehouse performance; implement transformations using SQL, stored procedures, and Snowpark. Monitor data quality, governance, security, deployments, and production issues. Collaborate with engineers, architects, analysts, and business stakeholders while automating workflows and contributing to data architecture and best practices.

Build, maintain, and support production data pipelines and platform capabilities using Snowflake, dbt, Airflow or Cloud Composer, APIs, files, and cloud services. Responsibilities include ETL/ELT development, data modeling, testing, deployment, monitoring, troubleshooting, optimization, documentation, and data quality. The role partners with analytics, data science, finance, risk, marketing, product, and engineering teams, while supporting reliable, scalable, and cost-effective data systems and emerging AI data workflows.

Design, build, and maintain data lakes, data warehouses, and end-to-end data pipelines for a multi-tenant SaaS platform. Lead code and design reviews, write infrastructure-as-code, optimize performance and scalability, participate in data governance, mentor junior engineers, evaluate new technologies, automate tests, collaborate cross-functionally, and join on-call rotation to ensure reliable data delivery.