Top Remote Analysis Reporting Jobs in Spokane, WA

Analyzes complex business data to identify trends, develop models, dashboards, and actionable insights. Collaborates with leaders and cross-functional teams, supports data governance, validates data integrity, applies statistical and predictive techniques, and communicates findings to technical and non-technical audiences. Provides mentorship to junior analysts and may lead projects. Occasional business travel may be required.

Lead advanced Amazon and commerce analytics using Amazon Marketing Cloud, advertising, and retail data. Build queries, dashboards, reports, tests, forecasts, and actionable client-ready recommendations. Partner with client, media, operations, data science, and reporting teams to support investment, optimization, and retail decisions. Ensure analytical quality, documentation, automation, and measurement rigor while identifying growth opportunities and communicating technical insights to clients.

The Senior Data Analyst will support Growth and Product teams through paid-channel analysis, user journey optimization, activation and retention insights, and churn reduction. Responsibilities include developing experiments, analyzing ad network performance, improving tracking and instrumentation, ensuring data quality, and enabling self-service analytics. The role requires translating complex data into actionable recommendations and communicating effectively across technical and non-technical teams in a fast-paced SaaS environment.

Owns performance analytics for General Liability claims processes, monitoring trends, diagnosing root causes, evaluating process improvements, and recommending priorities to leadership. Partners cross-functionally with claims, actuarial, underwriting, data, and field operations teams. Presents metrics and action items, validates deployed solutions, supports process optimization, and translates customer and business needs into actionable data-driven solutions.

Design, build, and optimize enterprise ETL/ELT pipelines, cloud data lakes, lakehouse solutions, data models, and semantic layers across AWS and Azure. Ensure data quality, security, performance, reliability, and governance while supporting analytics, reporting, and AI initiatives. Collaborate with product, engineering, IT, and business stakeholders to translate requirements into scalable data solutions, monitor pipeline health, troubleshoot issues, and improve data architecture and practices.

Lead full-cycle customer data migrations from legacy DME/HME systems into a SaaS platform. Responsibilities include mapping, extracting, cleansing, transforming, and validating patient, inventory, claims, order, and payer data; resolving discrepancies; collaborating with implementation and product teams; supporting customers; and documenting migration processes.

The Business Intelligence Engineer manages enterprise data sources, supports data quality and governance, and translates transactional data into KPIs for business teams. Responsibilities include guiding analyst teams, handling data inquiries, supporting advanced analytics, monitoring data performance, defining data requirements with stakeholders, and presenting findings to technical and non-technical audiences. The role requires SQL, Tableau, relational database, and data modeling or analytics expertise.

Own Step’s analytics data models, transformation pipelines, metric definitions, semantic layer, and data quality practices. Partner with Product, Marketing, Engineering, Risk, Growth, and Finance to establish trustworthy data foundations and enable self-service analysis. Build reusable query, visualization, and code components; apply AI-augmented workflows; and collaborate with data scientists, analysts, and platform engineers. The role requires strong SQL, modeling, pipeline, BI, communication, and independent ownership skills, with occasional travel for team onsites.

Conducts econometric, causal, statistical, and optimization analyses to improve operational efficiency, resource allocation, product performance, and business outcomes. The role defines efficiency measures, develops forecasting and optimization tools, partners with product and engineering teams on data instrumentation and implementation, and communicates findings to executives through clear reports.

Leads the design, development, governance, and productization of enterprise data models, pipelines, data products, and ontologies. Builds machine-learning-ready datasets, implements security and lineage controls, optimizes Databricks and Delta Lake environments, establishes data-product standards, collaborates with architects and data scientists, and mentors engineering talent. The role also defines product roadmaps, service levels, usage metrics, and continuous-improvement processes for enterprise data assets supporting AI agents, analytics, and applications.