Top Remote Data & Analytics Jobs in Omaha, NE

The Business Intelligence / Data Analyst will optimize the data reporting pipeline using Google Big Query and Omni, work with product management, implement reports, and conduct ongoing maintenance.

As an Analytics Lead, you will manage the measurement strategy, oversee tracking architecture, design testing frameworks, and create insights for clients. You'll lead a junior specialist and ensure data integrity across client accounts. Responsibilities include building dashboards, interpreting metrics, and creating measurement plans to drive business decisions.

The Sr. FWA Analyst identifies fraud, waste, and abuse in medical billing through data analysis, supporting engineering and data science teams with validation and tuning of detection algorithms.

Design and deliver Databricks-centric data architectures, lead pre-sales and billable engagements, build end-to-end data engineering pipelines for AI/ML and BI, mentor teams, ensure data governance/security/quality, and resolve data system issues while producing solution documentation and presentations.

Lead Oracle Cloud reporting initiatives, ensuring data accuracy and performance while mentoring junior analysts and collaborating with stakeholders.

The IBM TM1 Developer will manage the SDLC for Finance applications, focusing on TM1 and migrating from Essbase, while collaborating with global teams.

The Data Analyst will build key data models and metrics for decision-making, partner with stakeholders for analytical insights, create datasets for self-service reporting, and ensure data reliability through quality checks.

Lead administration and optimization of the Snowflake data warehouse. Design and enforce data management and security policies, tune performance and costs, lead migrations and ETL initiatives, manage Snowflake roles/SSO, mentor DBAs, and maintain architecture documentation while collaborating with data engineers and analysts.

Design and run evaluation frameworks to assess third-party vendor data for fraud detection. Build lift analyses, backtests, and champion/challenger tests; profile vendor datasets for quality and coverage; collaborate with fraud, product, and data engineering to define ingestion and testing requirements; translate findings into actionable recommendations and maintain an internal knowledge base; support ad hoc analyses on fraud trends and model performance.

Lead revenue and pricing optimization by analyzing pricing, customer behavior, unit economics, and marketplace dynamics; design experiments (A/B tests, cohorts), monitor KPIs, detect revenue leakage, and partner cross-functionally to deliver data-driven recommendations that increase profitability and engagement.