Top Remote Data & Analytics Jobs in Portland, OR

Build network capacity and demand forecasts across 50 states, support clinician recruitment and retention analytics, measure clinical and scheduling outcomes, and design experiments with Product and Engineering. Develop governed data models, KPI definitions, and AI-powered analytics infrastructure. Serve as the primary analytics partner for Network leadership by translating complex, multi-source data into actionable recommendations and presenting insights to technical and executive audiences.

Develop dashboards, reporting, and analytics solutions for business teams; monitor performance trends, data quality, risks, and key metrics; investigate complex questions; support A/B testing and instrumentation; and communicate actionable insights to technical and non-technical stakeholders. The role also requires expertise in profit drivers, unit economics, campaign and funnel performance, and the company’s analytics infrastructure.

Research and monitor political news, government sources, candidates, officeholders, and elections. Update Ballotpedia articles and databases, verify data accuracy, conduct jurisdictional research, write and proofread neutral content, maintain tracking spreadsheets, and collaborate across teams. The role requires strong political knowledge, organization, communication, project management, attention to detail, and flexibility for occasional evening and weekend work.

Data Engineer II will modernize BlueLabs’ data platform by leading Kimball dimensional modeling and legacy warehouse migrations, building reliable production data pipelines, and enforcing data quality standards. The role requires advanced SQL, Python, and dbt expertise, hands-on Snowflake and Redshift experience, and ownership of testing, documentation, observability, and scalable architecture. The engineer will collaborate cross-functionally, guide Data Engineer I team members, contribute to architectural decisions, and translate business logic into maintainable, cloud-agnostic data layers.

Combines GIS, business intelligence, and commercial real estate strategy to support portfolio planning and location decisions. Develops ArcGIS maps, dashboards, data pipelines, reporting solutions, and portfolio analytics. Integrates and validates data from multiple sources, supports workplace and occupancy strategy, automates reporting, and creates client-ready plans and recommendations. Partners with transaction teams, brokers, account leaders, and clients to translate business requirements into actionable scenarios and executive-level insights.

Own TAP’s data layer end to end, including ETL/ELT pipelines, BigQuery and dbt models, data cleaning, entity matching, quality monitoring, documentation, dashboards, and automated reporting. Investigate data delivery issues, improve reliability, use AI-assisted development responsibly, and answer program and leadership questions. As the first data hire, help establish standards, roadmap, and processes while collaborating across the organization.

Develop and maintain Python scripts and automated workflows for NLM metadata management. Integrate Alma, OCLC, and other systems through APIs and batch exchanges; process, transform, validate, reconcile, and analyze bibliographic and authority metadata in formats including MARC, XML, JSON, and CSV. Implement logging, exception handling, reporting, and documentation while supporting workflow testing, systems integration, and automation improvements.

Leads insight development, data analysis, reporting, and storytelling for consumer and telecom market research projects. The role manages client relationships, guides research design, presents actionable findings, creates client-ready deliverables, and provides consultative recommendations. It also leads project insight teams, supports proposals, coordinates with project management and sales teams, and manages multiple concurrent client engagements while ensuring analytical quality and process integrity.

The AVP, Data Engineer will design and build data ingestion, transformation, and replication pipelines using SQL, Python, dbt, and Dagster. Responsibilities include translating requirements into technical designs, developing data products, debugging production issues, supporting testing, and improving data engineering practices. The role also involves data modeling, mentoring junior engineers, conducting code reviews, and providing technical leadership while remaining hands-on. Experience with modern data platforms, workflow orchestration, cloud ecosystems, and insurance or financial services is preferred.

Develop and maintain Power BI reports, dashboards, visualizations, semantic models, and analytics solutions. Translate business requirements into scalable technical solutions, optimize queries and data models, ensure reporting accuracy and performance, and support data governance. Serve as the primary liaison for business users, provide training and troubleshooting, manage tickets, and collaborate with Data and QA teams. The role also advances AI and machine learning capabilities, mentors colleagues, and contributes to automation, documentation, and code reviews.