Top Remote Data & Analytics Jobs in Albuquerque, NM

Own executive, board, investor, financial, and operational reporting for leadership. Build scalable dashboards, define consistent KPIs, reconcile data across systems, investigate performance changes, and translate complex business questions into actionable insights. Partner with Strategy, Finance, and senior leaders to automate recurring reporting, improve data accuracy, and support actuals-versus-budget-versus-forecast analysis.

Owns analytics for BILL’s payments products, setting the measurement agenda, defining KPIs and OKRs, building production-grade dbt models, and conducting experimentation and causal inference. Partners with Product, Engineering, Finance, Marketing, and leadership to identify opportunities, improve data quality, automate reporting, and influence roadmap decisions. Presents executive insights and defends metric definitions and recommendations.

Processes client-requested data changes, creates data transfer packages, and writes SQL queries against Microsoft SQL databases. Conducts risk assessments, verifies data updates and transfer datasets, creates stored procedures, monitors data across databases, and supports clinical study implementation. Requires strong attention to detail, documentation, communication, prioritization, and occasional after-hours technical support.

Build customer-facing data products for the single-family rental market. Responsibilities include architecting real estate data models, designing reliable data pipelines and workflows, shaping data platform architecture, establishing engineering standards, and developing real estate domain expertise. The role partners with data science and product teams and requires leadership in scalable data engineering.

Designs and implements scalable data pipelines, cloud data platforms, storage solutions, and analytics infrastructure. The role focuses on Snowflake and AWS architecture, migrating legacy systems to cloud environments, developing Python and SQL data-processing logic, orchestrating workflows, and implementing Infrastructure-as-Code and CI/CD. The engineer also contributes to data governance, quality, observability, stakeholder communication, mentoring, and technical leadership.

The Senior Data Analyst will develop and improve Sigma dashboards, reporting, and business intelligence capabilities. Responsibilities include gathering stakeholder requirements, querying and analyzing complex datasets with SQL, improving data quality and governance, defining metrics, automating reporting processes, and partnering on Snowflake data modeling and warehouse improvements. The role also evaluates analytics tools, delivers actionable insights, and communicates findings to technical and non-technical audiences.

Monitors, validates, and improves data quality across device, program, operational, and field sources. Designs automated checks, reconciles discrepancies, investigates root causes, documents findings, and communicates impacts and recommendations. Collaborates with analytics, engineering, MEAL, and country teams to resolve issues. Maintains reproducible analyses using SQL, programming, version control, and notebooks while supporting data governance, reporting standards, and responsible handling of sensitive information.

Lead enterprise data engineering efforts supporting defense missions. Responsibilities include prioritizing technical tasks, mentoring engineers, conducting code reviews, translating government requirements into scalable data architectures, developing Databricks ETL/ELT pipelines with Python, PySpark, and SQL, troubleshooting pipeline performance and reliability, and documenting systems. The role collaborates with government stakeholders, product owners, data scientists, analysts, and technical teams to deliver secure, high-throughput data platforms and actionable insights.

Leads the design and delivery of enterprise-scale data, analytics, AI, Microsoft Fabric, Power BI, and data-driven application platforms. Owns analytics architecture, semantic modeling, data engineering, AI integration, governance, security, engineering standards, and technical roadmaps. Drives measurable business impact through KPIs and insights while leading architecture reviews, developing reusable patterns, and building analytics engineering, BI, data science, and technical leadership teams.

Designs and maintains enterprise data architecture, including data lakes, warehouses, integrations, migrations, orchestration, and data management protocols. Partners with architects, DBAs, BI teams, product leaders, and business stakeholders to support analytics and data science. Requires deep experience with cloud data environments, Azure SQL, Microsoft Fabric, Spark, Hadoop, PySpark, SparkSQL, security protocols, and enterprise data integrations.