Top Senior Data & Analytics Jobs in Albuquerque, NM

Leads commercial business intelligence strategy, Power BI reporting, dashboards, analytics, data governance, CRM optimization, and sales process improvement. Manages two direct reports and partners with commercial, finance, and operational leaders to improve revenue performance, forecasting accuracy, data quality, and commercial effectiveness. Oversees sales incentive compensation analytics and cross-functional system transformation initiatives. The role is fully remote with 10% overnight travel.

Own B2B pipeline and B2C growth analytics, including attribution, acquisition, registration funnels, SEO performance, forecasting, dashboards, experimentation, and marketing measurement. Partner with marketing, growth, product, sales, and data engineering to establish reliable metrics, improve reporting, guide investment decisions, and communicate actionable insights. The role also contributes to dbt models, semantic layers, AI-assisted analytics, and analyst mentorship.

Designs and maintains scalable enterprise data pipelines, models, and data products supporting analytics, reporting, Power BI, AI agents, and machine learning. The role implements layered data architectures, ensures data quality and performance, integrates data across platforms, troubleshoots pipeline issues, and collaborates with architects, analysts, and product owners. AI-assisted development tools such as GitHub Copilot and Claude are used to build AI-ready data solutions.

Leads the enterprise analytics organization, managing Analytics Engineers, Data Analysts, and BI Developers. Defines analytics strategy, delivers executive dashboards and operational reporting, establishes KPI and governance standards, builds semantic models and curated datasets, and drives self-service BI adoption. Partners with Engineering, Product, Finance, Operations, Customer Success, and executives to translate business needs into trusted analytical solutions while developing talent and prioritizing analytics initiatives.

Leads the enterprise analytics organization, managing Analytics Engineers, Data Analysts, and BI Developers. Defines analytics strategy; delivers executive dashboards, KPI scorecards, operational reporting, semantic models, and self-service BI. Establishes data governance and metric standards, ensures data quality, partners with Engineering, Product, Finance, Operations, and executives, and translates business questions into actionable analytical solutions. Mentors analytics talent and drives prioritization, adoption, and measurable business outcomes.

Analyze medical claims (inpatient, outpatient, SNF, rehab, professional, DME) to identify utilization and root causes. Pull/integrate data, perform QA, present findings, maintain attribution methodology, coordinate stakeholders, and develop tools. Primary coding: Hive, SQL, Python.

Leads the design and development of Mozilla’s data platform, processing tens of terabytes daily. Builds GCP workflows using Dataflow, BigQuery, Pub/Sub, GKE, and Airflow; evaluates technologies; establishes engineering standards; partners with Data Science, Product, Legal, Policy, and Engineering; develops data abstractions; mentors engineers; and supports evolving data products.

Own and evolve Looker as the company-wide business intelligence platform. Build dashboards, data models, pipelines, and integrations connecting Snowflake and other sources. Ensure data quality and provenance, support operational reporting, and partner with Finance, Data Science, and business teams. Help develop AI- and LLM-based agentic reporting tools that enable self-service analytics. Communicate technical concepts across the organization and provide insights for operational decision-making.

Leads complex analytics initiatives and partners with business leaders to define problems, analyze pharmacy, sales, and operational data, and develop actionable recommendations. Manages engagements across the full analytics lifecycle, from discovery through solution delivery and implementation support. Designs metrics, dashboards, frameworks, and decision-support tools while communicating insights to executive, operational, and technical audiences. The role emphasizes consultative analytics and business problem solving rather than software development or data infrastructure engineering.

Designs and builds production data systems, pipelines, database schemas, analytical models, and internal platforms. Responsibilities include writing tested code, optimizing queries and storage, controlling infrastructure costs, enforcing CI/CD, and implementing data testing, monitoring, and alerting. The role also develops AI and LLM-backed data use cases, including retrieval pipelines, embeddings, vector storage, evaluation harnesses, and agentic workflows.