Top Remote Data & Analytics Jobs in Lincoln, NE

Design, develop, and maintain Power BI semantic models, dashboards, paginated and SSRS reports. Build DAX, Power Query transformations, ETL processes (SSIS/Azure Data Factory), and semantic/tabular models. Engage stakeholders, perform testing/UAT, document solutions, tune performance, and support multiple client engagements in a consulting environment.

Design, build, and automate scalable batch and streaming data pipelines, implement data models and ETL, automate deployments, support ML/experimentation, maintain server architecture, and partner with stakeholders to deliver analytics and data-driven tools.

Lead and manage institutional research activities: collect, analyze, validate, and report quantitative and qualitative data; support program/course reviews, accreditation, dashboards, and decision-making; supervise a team and manage ad-hoc and ongoing reporting and assessment projects.

The Senior Analyst, Revenue Operations is responsible for delivering analytical insights, enhancing forecasting accuracy, and optimizing revenue processes while collaborating with various departments.

As a Senior Data Engineer, you will design and maintain data warehouses, ensuring data quality, manage data transformations, and mentor others while partnering with various stakeholders.

As a Data Analyst Intern, you will assist in data collection, cleaning, analysis, and visualization to support business decisions in a remote setup.

The ETL Architect is responsible for designing ETL architectures, development, and implementation of data models and warehousing solutions, with a focus on data quality and transformation logic.

The VP of Data will lead the data organization encompassing engineering and analytics, drive strategic insights, improve operational excellence, and integrate AI into workflows while ensuring data governance.

As a Senior BI Data Analyst, you'll analyze complex datasets, build SQL queries, develop predictive models, and create dashboards to drive insights and recommendations.

Support to evaluate and implement scalable data infrastructure. Work with technical teams to optimize data delivery and build reliable data systems. Expertise in data modeling, integration patterns, and data quality assurance is essential.