Top Remote Data & Analytics Jobs in Albuquerque, NM

Lead enterprise and domain data modeling across conceptual, canonical, logical, physical, and business models. Translate business concepts into scalable data structures, facilitate stakeholder workshops, establish business glossaries, and align models with governance, metadata, MDM, analytics, AI, reporting, and data stewardship initiatives. Support engineering teams implementing cloud and lakehouse solutions while serving as a domain architecture lead for durable, flexible modern data platforms.

Designs, develops, deploys, maintains, and optimizes ETL processes and cloud-based system integrations for healthcare data warehousing and business intelligence. Works with DHA and Cerner inpatient and outpatient data, develops database procedures, validates migrations, performs data mapping and modeling, and supports data science and machine learning teams. The role also documents processes, tunes performance, captures client requirements, and manages data transformation initiatives.

Investigates data governance issues using information management tools and data sources, identifies root causes, and partners with data owners, clients, vendors, and architects on resolution. Establishes governance baselines, standards, metrics, audits, and reporting. Defines governance requirements for data elements, integrates governance tests into ETL processes, communicates governance benefits, and drives continuous improvement of data management systems, policies, and processes.

Leads enterprise data architecture across Federal Student Aid’s Common Data Model, CRM platforms, servicer integrations, analytics, and downstream systems. Defines canonical models, systems of record, data contracts, integration patterns, governance, lineage, quality, security, privacy, and lifecycle controls. Develops reference architectures, decision records, modernization roadmaps, and data-flow designs; evaluates architectural tradeoffs; supports leadership reviews; collaborates across engineering, integration, security, analytics, product, and business teams; and mentors technical staff.

Designs enterprise Power BI semantic models and executive dashboards, translating ambiguous business questions into KPIs and visual narratives. Integrates data from enterprise sources, improves data quality with engineering teams, and establishes governance for accurate, auditable reporting. Mentors BI professionals, defines modeling and metric standards, optimizes performance and scalability, and influences senior stakeholders through trusted insights.

Design, build, and maintain scalable Snowflake data pipelines and workflows. Ingest, transform, and deliver data from EHRs, claims, APIs, and CRM systems into analytics-ready structures. Optimize Snowflake performance and costs through warehouse sizing, query tuning, data modeling, and security design. Implement orchestration, DevOps, version control, and CI/CD practices while collaborating with client architects and analysts. The role also involves explaining technical tradeoffs, supporting repeatable deployments, adapting to unfamiliar challenges, and mentoring others.

Document and reverse-engineer the client's ServiceNow data model and ITSM/CMDB processes. Design API-based incremental extraction into a Bronze layer and specify Bronze->Silver->Gold mappings. Define DNA crosswalk keys, data-quality gates and KPI confidence scoring, and support pipeline development, observability, and automated testing for ServiceNow as a source system.

Lead data analysis for product and customer decision-making by defining metrics, measuring adoption, engagement, retention, and customer health, and translating complex data into actionable insights. Partner with product, engineering, customer success, and sales teams; automate and improve data processes; support product strategy and customer reviews; strengthen data governance; and mentor analysts.

Lead and develop Analytics Engineers responsible for shared data foundations, platform capabilities, semantic models, developer tooling, and AI-enabled workflows. Define the team vision, operating model, roadmap, standards, and success measures. Partner cross-functionally to prioritize initiatives, improve adoption, guide architecture, and deliver scalable data assets and automation. Drive responsible AI-assisted development, data reliability, testing, documentation, observability, and cost management while contributing hands-on when needed.

Leads enterprise data engineering and platform execution, including modernization roadmaps, cloud transformation, governance, reliability, operational excellence, budgets, vendors, and investment prioritization. Directs multiple engineering managers and teams across platform engineering, data engineering, integration, and operations. Partners with architecture, security, compliance, analytics, clinical, product, finance, and business leaders to deliver secure, scalable, resilient data capabilities supporting AI, interoperability, reporting, and digital transformation.