Top Business Intelligence Jobs in Albuquerque, NM

The intern will gain experience in data visualization, quality checks, and reporting, collaborating with cross-functional teams to improve analytics and data governance.

The Technical Lead in Business Intelligence will improve data infrastructure performance, automate processes, support internal analytics, and collaborate with software engineers.

The Director of Business Intelligence at Viral Nation will lead analytics strategy for a CPG organization, driving insights and growth opportunities across sales, marketing, and digital engagement. Responsibilities include building a high-performing team, developing predictive models, and optimizing measurement strategies to inform executive decisions.

The Business Intelligence Analyst II role focuses on analyzing data, providing intelligence to business decision-makers, creating reports and dashboards, and ensuring data quality across departments. This position requires advanced analytics skills and expertise in data visualization tools.

Design, develop, and maintain enterprise Power BI dashboards and reports. Integrate data from SQL Server, Azure, Excel and other sources, perform data modeling and DAX calculations, validate data, and document data models and reporting logic while partnering with business stakeholders.

Lead enterprise Power BI architecture and governance, design scalable semantic models and high-performance reports, enable self-service analytics, optimize large-dataset performance, and mentor analytics teams while aligning solutions with business requirements and data engineering.

Design and maintain ELT pipelines, build dimensional (star/snowflake) analytics models, implement data transformations and quality checks, document data lineage and governance, partner with BI users, and mentor junior engineers to deliver performant analytics for business reporting and KPIs.

Build and maintain dashboards and reports (Excel, Tableau, Sigma) by pulling and modeling production data (SQL, Python). Create pricing models for RFPs, define operational and financial metrics, optimize queries and data structures, run A/B experiment analyses, and partner with business leaders to generate insights and recommendations to improve roadside assistance operations and performance.