Top Remote Data & Analytics Jobs in Lincoln, NE

The Solutions Engineer for media will curate and validate datasets from Protege's catalog, collaborating with sales and partners to meet customer AI data needs effectively.

The Business Analyst will gather requirements from clients, support project lifecycles, conduct demos, and manage change, while utilizing ServiceNow. Responsibilities include documentation and relationship building.

Lead a small BI team while remaining hands-on: write advanced SQL, build dashboards and models, enable self-service analytics, set governance, surface insights for stakeholders, and shape a BI roadmap that scales with alerting, anomaly detection, and applied AI/ML.

As a Compliance Data Scientist Co-op, you will automate data processing, enhance Power BI reports, and utilize AI for compliance improvements.

As a Data Engineer, you will build and optimize data pipelines, ensuring accurate data delivery that informs pricing and market analytics at Alt.

The Product Analyst will measure results and deliver insights on new AI products, identify opportunities, and guide product strategy through data analysis and visualization.

The Senior Consultant will lead the design and implementation of data solutions using Microsoft Fabric, Power BI, and Databricks to enhance analytics capabilities for clients. They will engage with stakeholders to define data strategies and deliver high-quality consulting services across multiple projects.

The Data Migration Specialist manages customer data migrations, including planning, executing, and validating data processes across various formats, collaborating with cross-functional teams to support implementations.

The Senior Bioinformatics Data Engineer will build data pipelines, implement clinical data ingestion, and optimize platform infrastructure while ensuring data quality and compliance with legal standards.

Build and own the reporting and insight layer: write optimized Redshift queries, build dashboards, monitor model feedback signals, triage and prioritize investigations for data science, maintain ETL pipelines ingesting semi-structured data, and enable Customer Success with clear metrics and onboarding checks. Move into model validation and automation to scale analytic workflows.