Top Senior Data & Analytics Jobs in Albuquerque, NM

The Senior Analytics Engineer will design and implement scalable data solutions for Finance Analytics, reconcile data accuracy, and collaborate across teams to enhance financial datasets and reporting workflows.

The analyst will own planning for launches, build forecasting models, partner with stakeholders, and drive data-driven decisions using AI and analytics.

Lead development and continuous improvement of EHS safety management systems and DriveSafe program. Oversee driver scorecards, performance analytics, dashboards, and ad hoc reporting. Partner with EHS, operations, IT, and leadership to align systems, ensure data governance, drive projects, implement EHS technologies, train users, support compliance, and use data-driven insights to reduce incidents and improve safety outcomes.

The Lead Data Analyst (Network) drives data-driven initiatives, dissects datasets, and provides actionable insights to enhance business performance and network efficiency.

Lead strategic vision and execution of GitLab's data platform and analytics capabilities, focusing on AI-enabled self-service analytics and data governance.

Designs, builds, and operates ELT data pipelines and analytical datasets for Platform Analytics. Curates and models source data into conformed datasets, ensures performance, reliability, and cost efficiency, contributes to target-state architecture, mentors junior engineers, and uses AI coding tools to accelerate development and delivery.

Design, build, and maintain scalable dimensional data models, semantic layer, and production transformation pipelines. Collaborate with Data Engineering, Data Science, BI, DevOps, and Product to enable AI-ready data, ensure data quality and governance, support ML feature delivery, and coach analytics team members for robust, production-ready data products.

The Senior Data Engineer will design and implement scalable data models in BigQuery using dbt for analytics and reporting, ensuring data governance and optimal performance.

Manage a team of data scientists to develop fraud detection models, lead planning and collaboration with leadership, and communicate outcomes effectively while maintaining strong business intuition and technical leadership.

Lead design and implementation of a unified event-tracking data platform. Collaborate with Data, Product, and Engineering to build reusable event-publishing frameworks, monitoring/alerting, and deprecate legacy systems. Coach mid-level engineers, influence roadmap and processes, manage cloud/SaaS spend, create documentation, and participate in on-call rotations to ensure platform reliability and data trustworthiness.