Top Remote Analysis Reporting Jobs in Las Vegas, NV

Build and operate cloud data capabilities, including ingestion pipelines, transformations, data models, curated data products, and data-quality controls. Use Snowflake, dbt, SQL, and Python to deliver governed data for analytics, applications, automation, and AI workflows. Implement testing, CI/CD, documentation, lineage, access controls, monitoring, incident resolution, and performance optimization. Collaborate with engineering, analytics, governance, security, and business teams in an Agile, product-oriented environment with active AI-assisted development.

Lead the technical direction, architecture, and roadmap for the company’s data platform. Design reliable large-scale pipelines, establish standards for modeling, testing, observability, and orchestration, and mentor data engineers. Partner with engineering, product, analytics, and business stakeholders to align infrastructure investments. Lead incident root-cause analysis, evaluate data technologies, and support scalable analytics, machine learning, and real-time processing capabilities.

Lead the design, development, optimization, and maintenance of scalable batch and streaming data pipelines. Build ETL/ELT workflows using Python, SQL, and AWS services; optimize data models and cloud data platforms; ensure data quality, integrity, reliability, and performance; establish CI/CD and infrastructure-as-code practices; resolve pipeline issues; document systems; and mentor junior engineers.

Own the digital measurement ecosystem across websites, quote engines, customer portals, and account applications. Lead Google Tag Manager tracking frameworks, data-layer standards, Google Analytics 4 configuration, conversion events, attribution, funnel analysis, experimentation measurement, data-quality audits, and analytics governance. Partner with Marketing, Product, Engineering, and Data teams to translate customer behavior into actionable insights that improve growth, conversion, and customer experience.

Own and maintain the companys data infrastructure and pipelines. Design, build, and monitor ETL/ELT workflows and data architecture; integrate SaaS sources; enforce data quality, standards, and governance; partner with stakeholders to deliver analytics-ready data and meet data availability SLAs.

Administers, designs, secures, monitors, and optimizes large-scale client/server databases. Responsibilities include database programming, performance tuning, backup and disaster recovery, access control, auditing, documentation, systems integration, and advising users. The role requires supporting database projects in large organizations, working across operating systems and cloud platforms, and maintaining sensitive data security. A Secret clearance and DoD IAT Level II certification are required.

Designs and maintains data warehouses, semantic models, metrics layers, pipelines, dashboards, and certified datasets. Establishes KPI governance, data security, row-level access controls, quality standards, and observability. Builds Sigma, Power BI, and Databricks dashboards, supports self-service analytics, resolves BI issues, and enables AI/ML use cases. Collaborates with architecture, engineering, analysts, and business stakeholders to define requirements, document solutions, train users, and drive BI adoption.

Own and enhance Sigma dashboards and business intelligence reporting for the Records Retrieval business. Partner with stakeholders to translate business needs into scalable data solutions, analyze complex datasets, improve data quality and governance, define reporting standards, and support Snowflake data warehouse and modeling initiatives. Deliver actionable insights, automate reporting processes, evaluate analytics tools, and independently drive analytics projects from requirements through delivery.

Loads, queries, extracts, validates, and refreshes client data; prepares reports and deliverables; identifies automation and process-improvement opportunities; supports UAT, system implementations, and quality assurance testing; and collaborates with cross-functional teams. The role translates client requirements into actionable tasks and requires SQL, relational database, Oracle, Unix, data analysis, reporting, and communication skills.

Own healthcare analytics data methodology, including episode grouping, provider attribution, cost standardization, quality measures, reference data, pipelines, QA, and production delivery. Translate feature requests into specifications and validated analytics. Reconcile results, establish recurring data-quality coverage, investigate failures, and collaborate with engineering, data science, product, clinical informatics, and customer teams. Advanced SQL and healthcare claims expertise are central to ensuring accurate, defensible customer metrics.