Top Analysis Reporting Jobs in Albuquerque, NM

Build and maintain scalable data pipelines and Python automations for ingesting, processing, and enriching data. Integrate SaaS sources via APIs, use Databricks/PySpark/Delta Lake and AI tools to power lead generation, enrichment, deduplication, and entity resolution. Collaborate cross-functionally to translate business needs into automated, production-ready solutions.

Design, build, and maintain ingestion and transformation pipelines for a knowledge-graph analytics platform. Profile and prepare structured and semi-structured source data, ensure data lineage and traceability, implement transformation logic for graph loading, collaborate with data scientists on graph modeling and entity resolution, improve pipeline reliability, and document ingestion patterns using version control and issue tracking.

As a Senior Data Scientist, you will develop statistical models and machine learning algorithms to improve patient enrollment and trial management while ensuring compliance in a healthcare data environment.

Lead analysis of business processes, gather and document requirements, design and implement system solutions, support deployments, and create reports/dashboards. Collaborate with stakeholders and IT to improve systems, provide user training, and perform post-implementation reviews to enhance performance and efficiency.

Owns and codifies business definitions and calculation methods for legal spend analytics, audits legacy Excel/PowerPoint artifacts, validates analyses (SQL and AI), supports reporting and storytelling for senior law department and C-suite audiences, and helps migrate analyses into modern analytics tooling.

Design, build, and maintain advanced Workday reports, dashboards, and analytics to inform workforce planning and talent strategy. Partner with HR, HRIS, IT, and business leaders to define metrics, validate and govern data, perform deep-dive analyses, support Prism integrations, and deliver executive-ready insights, documentation, and training to enable data-driven HR decisions.

Design, implement, and maintain dbt/Snowflake ELT transformations to convert raw clinical and multimodal health data into trusted, analysis-ready models; enforce data quality, lineage, testing, CI/CD, and documentation; collaborate with clinical and ML teams to harmonize ontologies and support de-identification and ETL across health systems.

Design, build, and maintain automation workflows and data pipelines across admissions, education, career services, and billing. Create BI dashboards, ensure data accuracy, manage lightweight databases (e.g., Supabase), and support cross-functional teams to improve operational efficiency and scalability.

Develop and optimize T-SQL scripts to transform legacy SIS data for loading into the Jenzabar platform, manage multiple conversion projects, communicate with clients and internal teams, adhere to project plans and timelines, estimate custom work, and improve conversion processes.

The Senior Data Engineer will design and build scalable data platforms, handle automation for data operations, and ensure reliability and performance of data pipelines while collaborating with various teams.