Top Remote Data & Analytics Jobs in Boise, ID

Provide analytics, automation, and reporting to support compliance testing and monitoring. Collect, validate, reconcile, and analyze large data populations; develop repeatable testing routines, dashboards, and automated solutions; support sampling, exception analysis, reporting, and stakeholder requests to identify trends, risks, and remediation status.

Analyze healthcare and clinical operations data to deliver actionable customer insights, physician performance reporting, care-quality dashboards, and scalable BI solutions. The role requires SQL-based data transformation, dashboard development, collaboration with clinical, product, engineering, and client-facing teams, and clear communication of findings. The analyst will also use AI tools for query generation, data cleaning, pattern recognition, and analytical summaries.

Develop and implement Databricks and AWS data-driven solutions supporting IRS tax-system modernization. Responsibilities include analyzing complex data, prototyping solutions, testing applications, conducting code reviews, debugging and improving software, responding to defects, and potentially leading engineering teams. The role requires collaboration in an Agile environment and communication with technical and client stakeholders.

Designs and implements the data architecture supporting engineering technology, analytics, automation, and AI. Assesses current systems, defines target architecture and governance, develops data models and ETL/ELT pipelines, integrates engineering platforms, and establishes metadata, lineage, stewardship, access, and quality standards. Partners with engineering and Digital Engineering teams to deliver reusable data assets, dashboards, and analytics solutions while coordinating data protection, platform evaluations, roadmaps, and stakeholder communication.

Build and maintain data lakes, data warehouses, and modern data pipelines for a multi-tenant SaaS platform. Design end-to-end data processes, write pipeline and infrastructure code, conduct design reviews, optimize performance, monitor systems, support data governance, and troubleshoot scalability and security issues. Collaborate with technical and business stakeholders, mentor junior engineers, evaluate technologies, document platform architecture, automate testing, and participate in on-call rotation.

Own the architecture, roadmap, and operation of Clarium’s AWS-based data platform. Build reliable batch and near-real-time pipelines, model healthcare supply chain data, optimize Postgres and Snowflake performance, and establish standards for testing, deployment, quality, and governance. Partner cross-functionally to create durable data models, lead multi-quarter initiatives, manage sensitive data controls, and mentor engineers.

Leads and mentors the Data Engineering team while remaining hands-on in designing, building, and maintaining scalable data pipelines, warehouses, integrations, and infrastructure. Establishes architecture, reliability, quality, and performance standards; guides technical delivery; and resolves complex issues. Partners with Finance and business stakeholders to translate data needs into practical solutions, sets technical direction, evaluates technologies, and communicates strategy, risks, and recommendations to leadership.

Leads the design and implementation of scalable data architectures, ETL pipelines, data warehouses, migrations, and cloud data solutions. Provides technical leadership through system analysis, requirements gathering, design documentation, code and architecture reviews, SDLC management, production deployments, and mentoring junior engineers. Develops integrations using multiple programming languages, ETL platforms, cloud services, APIs, and CI/CD tools.

Leads and delivers complex data engineering projects, designing scalable data infrastructure, pipelines, warehouses, and data quality solutions. Recommends tools and architectures, optimizes data movement and performance, collaborates across technical teams, mentors engineers, and translates technical concepts for clients and nontechnical stakeholders. The role may also involve real-time processing, data modeling, transformation, security, and MLOps.

Analyze large healthcare datasets, build and implement quantitative models, perform statistical analyses, ensure data accuracy, collaborate with cross-functional teams, and provide actionable insights to improve healthcare delivery.