Top Data & Analytics Jobs in Las Vegas, NV

Lead architecture and implementation of cloud-native data platforms on Google Cloud (BigQuery). Build unified batch/streaming pipelines, enforce IaC with Terraform, implement open table formats and governance (Dataplex/Data Catalog), automate data quality/lineage, partner with analytics and product teams, and mentor data engineers.

Analyze complex datasets to detect and prevent fraud, build dashboards, optimize queries, and collaborate with cross-functional teams in risk management.

Lead data conversion processes, provide expert guidance on Oracle Cloud, and ensure the successful implementation of conversion solutions while collaborating with stakeholders.

As a Principal Data Engineer, you will design and implement data infrastructure and solutions, collaborating across teams to enhance healthcare data usage.

Lead the vision and execution of a scalable, multi-tenant data platform: own ingestion, warehouse, transformation, BI, and AI-on-data layers. Manage and grow a data engineering team, define engineering standards (CI/CD, infra-as-code, testing, lineage), productize integrations, and collaborate with Product, Engineering, and GTM for client onboarding and data delivery.

The Data Architect will design scalable data solutions, enforce data standards, lead architecture discussions, and collaborate with stakeholders. Responsibilities include managing platforms, ensuring data compliance, optimizing pipelines, and guiding teams toward data-driven solutions.

Build reliable data models and semantic layer for AI-powered analyses. Support Product, Finance, and Customer Success with SQL-based analytics, BI dashboards, and stakeholder storytelling. Standardize data team processes, prioritize work, and collaborate across company to enable data-informed decisions.

Design and advance enterprise data and BI architecture: optimize OLTP/OLAP models, establish modeling/integration/governance standards, lead POCs, mentor teams, enable AI/ML, and guide cross-functional implementation from concept to production.

Embed within a core business area to define problems, build metrics/reports, run analyses and experiments, surface insights, and drive recommendations to deliver measurable business outcomes across growth, product, AI, and partnerships.

Own analytics for an embedded business unit: define problems and metrics, build reports, run analyses, design experiments, and drive cross-functional recommendations to improve growth, product, AI, or partnerships.