Top Senior Analytics Jobs in Albuquerque, NM

Manage people analytics, conducting data analysis to inform HR strategies, offer recommendations for improvement, and create reporting dashboards. Collaborate with HR and business leaders to align people data with business needs.

Design, develop, and maintain Power BI dashboards and automated reports for financial analytics. Ensure data accuracy, build data models and ETL pipelines, write/optimize SQL and DAX, analyze trends to provide insights, and collaborate with stakeholders to define reporting requirements and best practices.

The Lead Risk Analytics Data Scientist will develop risk pricing, design data models, automate workflows, and communicate insights to leadership. Responsibilities include owning the risk narrative, building analytics foundations, and utilizing AI to enhance efficiency.

Embed on the Video product team to build reusable data models, transformation layers, and pipelines (Snowflake, dbt/SQLMesh). Deliver production-ready data products (APIs, dashboards), instrumentation, experimentation infrastructure, and predictive/causal models to inform product, retention, pricing, and growth decisions.

Design and maintain a dbt-based business data layer for GTM metrics, centralize and reconcile multi-source data, implement automated data quality and reconciliation checks, align and document metrics with stakeholders, mentor analytics team, and lead root-cause investigations into complex data anomalies.

As a Senior Analyst, you will develop data-driven solutions in collaboration with operations teams and deliver insights through reports and dashboards to improve patient outcomes.

Design, build, and maintain production ETL/ELT pipelines, analytics-ready data models, and cloud data infrastructure. Improve pipeline performance, data quality, governance, and migrate legacy systems to modern platforms. Collaborate with stakeholders to translate business requirements into reliable data products for project, financial, and operational analytics.

Design and build scalable data foundations, pipelines, semantic models, and AI-grounded tooling. Lead platform-level AE tooling, reusable dbt patterns, observability, cost strategies, and agentic AI workflows. Partner with stakeholders, guide team technical development, and drive cross-functional data initiatives from ambiguous problems to production.

Design and implement end-to-end Microsoft Fabric data solutions (ingestion, Lakehouse, warehouse, real-time). Create architectures, reusable frameworks, and governance patterns for insurance datasets. Lead ingestion pipelines, optimize performance/cost, integrate with Azure services and security, support regulatory compliance, and provide technical direction to engineering and analytics teams.

Design and implement end-to-end data solutions on Microsoft Fabric and Azure for insurance datasets. Build ingestion pipelines (batch, streaming, API), Lakehouse architectures, and data models. Enforce governance, security, compliance, and cost/performance optimizations while guiding engineers and stakeholders on architectural best practices.

Join the BizOps & Analytics team embedded in a core business area to define problems, build analyses and reports, design experiments, surface insights, and drive data-informed decisions end-to-end to increase customer growth and business scale.

Embedded on a core business unit, own end-to-end analytics: define problems, design metrics and experiments, pull and analyze data, build reports, surface insights, and drive recommendations to inform product, growth, AI, or partnership decisions. Influence stakeholders and execute on outcomes.

Embed with a core business area to define problems, build metrics, run analyses and experiments, generate actionable insights, and drive product, growth, AI, or partnership outcomes end-to-end.

Lead end-to-end analytics for an assigned business area (growth, product, AI, or partnerships). Define problems, build metrics, reports, experiments, and analyses, surface insights, and drive recommendations and execution to improve key business outcomes.

Build and maintain data infrastructure, integrate veterinary partner systems, develop dbt models and pipelines, provide analytics modeling, testing, reporting, and deliver actionable insights to customers and internal teams.

Lead and grow a BI and analytics team to build scalable, AI-enabled reporting and self-service analytics. Partner with cross-functional stakeholders to deliver executive dashboards, standardize metrics, ensure data quality and governance, and use AI tools to automate insights and accelerate dashboard development.

Lead a small analytics team (staff data scientist + two analysts) to deliver curriculum, product, and program insights. Split time between people management (60-70%) and hands-on analysis (30-40%), advance experimentation and measurement, build AI/LLM-driven agentic workflows, manage sprints and cross-functional stakeholder relationships, and drive analytics that inform curriculum, product, and intervention strategies.

Design, build, and own Sardine's internal data platform and billing infrastructure. Implement and maintain ETL/ELT pipelines, warehouse partitioning, access controls, and data governance. Partner with stakeholders to define KPIs, produce dashboards, and ensure data quality for executive, GTM, product, and finance teams.

The Senior Manager of Risk Analytics will lead credit risk strategies, manage portfolio performance, optimize decisioning frameworks, and drive business growth through analytics and predictive modeling.

Manages clinical data analytics, providing expertise in areas like risk adjustment and quality measures, mentoring staff, and driving operational improvements. Oversees analytics support and communication with clinical leaders, ensuring effective data usage for strategic decision-making.

Oversees analytics reporting and provides critical analysis to support strategic decisions across various business functions. Leads teams, develops reporting solutions, monitors metrics, and manages business processes.

The Senior BI Analytics Engineer will design and deliver data models for analytics, ensuring data quality, and collaborating across teams to enhance AI-driven reporting and querying capabilities.

Lead operations analytics for fulfillment: build and own KPIs and dashboards, detect inefficiencies, deliver actionable recommendations, and develop forecasts and capacity models to guide operational decisions.

Build and maintain production data pipelines and foundations for People Analytics and AI-driven tools. Integrate HR systems (Workday, Greenhouse), design data models, ensure governance for sensitive employee data, support data science and reporting, and deliver dashboards and Streamlit apps. Optimize queries, enable LLM consumption with quality and latency controls, and collaborate cross-functionally to transition AI prototypes to production.

Build and maintain dbt data models, design BI dashboards and reports (Count and other tools), and deliver merchant/issuer analytics. Partner with GTM and product teams to translate data into actionable insights, improve data quality, and lead technical investigations to scale analytics infrastructure.