Top Analysis Reporting Jobs in Portland, OR

Conduct AI-powered web research, scraping, market analysis, and lead generation to identify clients, partners, sponsors, investors, and collaborators. Build and maintain intelligence databases, automate research workflows, verify and enrich data, analyze unstructured information, prioritize opportunities, and present actionable recommendations to leadership.

Develop and maintain Palantir Foundry data pipelines, transformations, Ontology models, workflows, data products, and user-facing applications. Integrate structured and unstructured data from enterprise systems and cloud platforms using Python and SQL. Implement data quality, governance, metadata, lineage, testing, and validation practices while collaborating with technical and business stakeholders. Support Agile development, documentation, modernization, AI-enabled analytics, troubleshooting, and federal data solutions.

Designs and implements big-data data management solutions (Cloudera/Hortonworks/Databricks). Builds and automates reporting and analytics using SQL and Databricks, optimizes queries, manages ETL, integrates multiple data sources, enforces data governance, and supports payment integrity reporting requirements.

Design, build, deploy, and maintain configurable, metadata-driven analytics platforms and workflow/decision engines for Risk Adjustment. Partner with analysts to translate processes into auditable, scalable solutions; own full lifecycle, production support, monitoring, and performance optimization while applying AI where beneficial.

Build and maintain performance dashboards, KPIs, and data frameworks for revenue operations. Analyze funnel performance and program effectiveness, translate complex data into leadership insights, automate recurring reporting, and perform SQL-based data wrangling and transformations. The role partners with cross-functional, engineering, and data science teams to improve measurement, resolve data issues, and support strategic business decisions.

Own paid social measurement and analytics across brand, consideration, and performance campaigns. Execute Conversion Lift and Brand Lift Studies, design geo-randomized experiments with data science, build KPI dashboards, evaluate creative and audience performance, and recommend budget and strategy changes. The role requires advanced SQL and Python or R, causal inference expertise, scalable reporting, and the ability to communicate insights to senior marketing leaders.

Design, build, and maintain scalable, documented data models and the Gold data layer using dbt/Coalesce. Ensure data quality through automated tests, observability, version control, and CI/CD. Optimize SQL and warehouse performance, collaborate with Data Engineering and Analytics, and support semantic layers and BI catalogs to enable trusted, self-service insights for Product, Finance, and GTM teams.

Deliver and interpret catastrophe modelling and exposure analytics across global insurance portfolios. Support accumulation monitoring, PML optimization, exposure data quality, reinsurance treaty analysis, portfolio reporting, and strategic risk decisions. Collaborate with underwriting, exposure data, ceded reinsurance, brokers, reinsurers, capital providers, and model vendors. Improve and automate catastrophe modelling, exposure management, and reporting workflows while translating complex results into actionable insights.

Designs, develops, and supports integration pipelines connecting SAP and non-SAP systems with SAP Datasphere and cloud data platforms, primarily Amazon Redshift. Builds data models, replication flows, ETL/ELT processes, and CDC integrations; monitors performance and data quality; applies governance and security standards; documents architectures; and collaborates with technical and business stakeholders to deliver scalable analytics solutions.

Clean, standardize, enrich, and import product data into the PIM while maintaining data integrity and governance standards. Oversee bulk ingestion processes, leverage AI and automation, support data model improvements, resolve product listing issues, and contribute to data hygiene and workflow modernization. The role also involves collaborating with client-facing teams, documenting processes, proofreading product data, communicating progress, and adapting to changing priorities.

Short-term, paid project to export and submit personal financial data from multiple apps. Participants must provide exports from specific financial categories, create two financial artifacts, redact PII on-device using a provided tool, and follow quality and timeline requirements until Final Approval.

Leads the architecture, design, and implementation of scalable GCP and Databricks data lakehouse platforms. Designs real-time and batch pipelines, data models, governance, cataloging, security, and performance optimization. Advises clients, translates business requirements into technical solutions, leads project teams, mentors engineers, reviews architectures, and balances strategic planning with hands-on proof-of-concept and troubleshooting work.

Lead data engineering and machine learning initiatives by designing, architecting, developing, and supporting GCP data pipelines. Create information architecture documentation, estimate and plan technical work, collaborate with project teams and business analysts, and translate requirements into technical designs. Develop Python-based ELT pipelines, optimize BigQuery SQL, support CI/CD, and provide technical leadership to projects and teams.