Top Remote Data & Analytics Jobs in Omaha, NE

Own the Finance and Operations data layer, integrating systems into Snowflake and Omni, building reusable data models, dashboards, governed metrics, validation checks, and reconciliation workflows. Partner with Finance, Operations, and Data teams to translate business processes into trusted analytics, improve data quality, automate reporting, and support leadership decision-making.

Analyzes healthcare claims, provider, pharmacy, and enrollment data to identify trends, improve data quality, optimize workflows, reduce costs, and support business decisions. Translates business needs into requirements, develops complex SQL queries, validates data, recommends process improvements, coordinates projects, and serves as a liaison among operations, research and development, IT, and external partners. Also mentors team members and communicates technical findings to nontechnical stakeholders.

Owns and codifies business definitions and calculation methods for legal spend analytics, audits legacy Excel/PowerPoint artifacts, validates analyses (SQL and AI), supports reporting and storytelling for senior law department and C-suite audiences, and helps migrate analyses into modern analytics tooling.

Lead architecture and hands-on engineering to design, scale, and govern a cloud-native lakehouse using Databricks and Microsoft Fabric. Build batch/streaming pipelines, ensure HIPAA-compliant PHI handling, produce analytics-ready datasets, mentor engineers, and drive platform strategy, governance, and reliability.

Own the architecture and hands-on development of Tala’s shared data and analytics platforms. Build semantic layers, governed business metrics, data models, pipelines, services, tooling, and AI-enabled analytics workflows. Lead cross-functional technical initiatives spanning Product, Engineering, Analytics, Finance, and Accounting; improve governance, reliability, usability, and documentation; resolve architectural bottlenecks; and mentor engineers through technical leadership.

Develops and manages enterprise business intelligence products, including semantic models, governed definitions, metadata, dashboards, and AI-assisted analytics capabilities. Translates complex business needs into measurable analytic solutions, advises stakeholders, supports adoption and training, and manages product lifecycle, quality, performance, and business impact. Partners with product managers and business leaders to prioritize work, manage risks, communicate value, and guide less experienced team members.

Analyze web, journey, survey, and marketing data to uncover actionable insights for enterprise clients. Translate technical findings into business recommendations, test-and-learn plans, dashboards, reports, and presentations. Collaborate with account teams, ensure data quality and statistical rigor, investigate anomalies, and support client questions. The role requires expertise in analytics platforms, data visualization, spreadsheets, SEO, and client-facing data storytelling in a fast-paced SaaS environment.

Own analytics, modeling, reporting, and recommendations for portfolio management. Design underwriting and pricing for multicard and line-extension strategies, test rewards upgrades and purchase incentives to boost engagement, and partner with risk, data science, and engineering to operationalize strategies and defend recommendations to stakeholders.

Leads enterprise analytical modeling and semantic architecture, establishes standards for testing, documentation, governance, lineage, observability, and access, and develops reusable governed data products. Partners across Product, Engineering, Finance, Clinical, Research, Marketing, and AI teams; guides architecture reviews, data quality ownership, semantic layer design, and governance adoption. Mentors engineers and drives modernization of analytical environments.

Build strategic data infrastructure roadmaps, design and implement complex ETL/ELT pipelines, and deliver scalable, secure data solutions. Collaborate across teams on cloud platform selection, data management, governance, privacy, and compliance. The role requires expertise in Databricks, Snowflake, data modeling, performance optimization, and pipeline automation.