Top Analysis Reporting Jobs in Reno, NV

Analyze, trace, reconcile, and migrate insurance policy data from legacy systems into a modern policy platform. Build source-to-target mappings and ETL transformations, perform data cleansing and validation, write complex SQL, use cloud databases (AWS RDS/S3), and collaborate with stakeholders using GitHub and Agile tools. Heavy daily use of AI tooling; mentorship provided throughout migration lifecycle.

Lead Salesforce administration and data quality efforts, manage data model transformations and integrations (FormAssembly, WordPress), handle support tickets and training, create documentation and user enablement, oversee external data collection processes, and collaborate with internal teams and external consultants to build a data-driven culture.

Design, build, and maintain Scala-based ETL and streaming data pipelines, APIs, and data microservices in Azure. Optimize LLM fine-tuning and RAG workflows, support DevOps/CI-CD practices, document standards, mentor engineers, and manage data ingestion frameworks for analytics and partner integrations.

Partner with Product and Engineering to turn product data into insights: define AI/agent metrics, design and analyze experiments, build dashboards and reports, perform cohort and behavioral analysis, surface opportunities, and guide product decisions with measurement and instrumentation improvements.

As a Senior Commercial Insurance Analyst, you'll perform complex analyses on data to drive business performance and risk outcomes. You'll collaborate with various teams, generate actionable insights, and mentor junior analysts, all while fostering a data-driven culture and improving operational efficiency.

Run funnel audits and synthesize multiple datasets into exec-ready narratives. Recommend and help launch A/B tests, benchmark against genres and market data, partner with game analysts and ETL engineers to validate and maintain clean data, and provide actionable UA, monetization, and live-ops recommendations to customers.

Lead development and ownership of end-to-end data pipelines and platform tools. Build and optimize ingestion, transform data with dbt and Jinja, implement monitoring/alerting, manage deployments and CI/CD, and create self-service analytics applications and internal tooling.

Support national community lending initiatives by developing ROI dashboards, analyzing event and CRM data, coordinating events and partners, enabling sales teams, managing budgets/contracts, and producing program performance insights to scale community outreach and homeownership programs.

Design, build, and maintain scalable ETL/ELT pipelines and Lakehouse solutions; ingest and transform diverse data formats; optimize Databricks and SQL Server environments; implement data quality, lineage, and governance; support batch and streaming ingestion; collaborate with analysts and stakeholders to enable analytics (fraud/anomaly detection, financial oversight); troubleshoot and improve pipelines. Candidates must be willing to undergo a U.S. government background investigation.

Partner with Product, Design, Engineering, and Data to define KPIs and measurement for AI-powered features, guide instrumentation, design and evaluate experiments, build dbt models, create dashboards, and influence product analytics standards and long-term strategy.