Top Analysis Reporting Jobs in Albuquerque, NM

Own analytics and reporting initiatives for EDU Partnerships, from defining business questions through delivery and adoption. Build scalable dashboards, reports, data products, and self-service resources; analyze partner health, performance, student engagement, risks, and opportunities; and deliver partner-facing insights. Collaborate with Product, Engineering, Data, Success, Sales, Marketing, Finance, and Support while communicating findings, improving data quality, and enabling stakeholders to make informed decisions.

Own pharmaceutical media performance reporting, analyze marketing data, develop dashboards, identify optimization opportunities, and present actionable insights to clients. Support attribution, test and control analyses, predictive modeling, AI-enhanced reporting, and process improvements. Collaborate with strategy, analytics, media, client services, data science, and technology teams to advance reporting capabilities and data-driven decision-making.

Manage a team of 2–3 analysts while remaining hands-on with SQL, dbt models, dashboards, and complex operational analyses. Lead ambiguous, cross-functional initiatives, deliver recommendations to senior stakeholders, ensure data quality, and improve reporting automation. Partner on AI analytics strategy, data readiness, agent evaluation, and human-in-the-loop governance. Responsibilities include coaching, prioritization, performance management, technical review, KPI development, and operational insights across customer experience, fraud, payments, workforce management, and VIP functions.

Organize and curate large volumes of raw information: extract key points from transcripts, maintain clean spreadsheets, curate links/resources, tag and categorize content, and follow or propose systems for information structure. Part-time remote role with initial 10 hours/week and potential to grow.

Build and operate production pipelines for legal records from PACER, court systems, opinions, PDFs, scans, XML, and HTML. Develop LLM-assisted extraction workflows, validate data quality, optimize SQL, and support RAG and semantic search products. Manage AWS S3 and RDS systems within security guardrails while collaborating with engineering teams. The role requires strong Python and SQL skills, legal data experience, document extraction expertise, and autonomous problem-solving with large, messy datasets.

Leads architecture and hands-on development of scalable data pipelines, governed data models, semantic layers, analytics products, and AI workflows. Writes production SQL and Python, establishes engineering standards, mentors engineers, and drives design reviews. Builds agentic and LLM solutions using retrieval and managed AI services while ensuring data quality, observability, reliability, privacy, and regulatory compliance for PHI and genomic data. Partners with stakeholders, platform teams, and governance forums to deliver trustworthy business solutions.

Leads analytics and data engineering for Sales and Revenue Operations. Builds reporting in Looker and Snowflake, manages CRM data flows into cloud warehouses, establishes transformation and modeling layers, and develops forecasts, scoring, churn, and expansion models. Supports territory, quota, pricing, and revenue-truth initiatives while presenting insights to finance and company leadership.

Analyze product, user, transactional, and repayment data to develop risk and underwriting models, reduce losses and fraud, monitor model performance, and guide product and growth strategy. Build Looker dashboards and LookML models, conduct segmentation and experimentation, measure CAC and LTV, and communicate actionable insights to product, engineering, marketing, finance, and leadership.

Lead ownership and evolution of a Microsoft Fabric-based data platform: design medallion/semantic architectures, build and optimize data pipelines, enable Power BI reporting, support SQL Server performance and API integrations, implement observability with Azure Log Analytics/KQL, and shape the data+AI roadmap.

Build and maintain data pipelines for network and operational telemetry, integrate DataMiner and other monitoring systems, create Power BI dashboards, configure monitoring metrics and alerts, automate reporting, analyze network performance and recovery events, and translate technical findings into actionable insights for technical and executive stakeholders.