Top Analysis Reporting Jobs in Portland, OR

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.

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.

Owns enterprise-level analysis of client sentiment, health, retention, churn, and journey data. Develops measurement frameworks, health scores, executive dashboards, root-cause analyses, and leading indicators tied to revenue. Synthesizes qualitative and quantitative feedback, identifies systemic experience risks, supports lifecycle improvement initiatives, and presents data-backed recommendations to senior leaders and cross-functional teams.

Support marketing analytics by analyzing digital channel performance, building and maintaining KPI dashboards, ensuring data accuracy, and collaborating with cross-functional teams to turn insights into actionable recommendations.

Reviews, develops, validates, and refines dashboards, reports, visualizations, calculations, data sources, KPIs, and analytical workflows across BI platforms. The role documents findings, provides structured feedback, troubleshoots reporting accuracy, and evaluates AI-powered agents and workplace connectors used in analytics processes.

Build, operationalize, and design complex data solutions for multiple customer accounts. Responsibilities include data transformation, cleansing, governance, security, quality, source analysis, testing data movement and transformation code, troubleshooting defects and incidents, and educating end users. The role also requires guiding customers on technical decisions, collaborating across teams, and supporting data products and analytical environments.

Design, build, deploy, and operationalize scalable data engineering solutions for customers. Responsibilities include developing pipelines, transforming and cleansing data, analyzing sources, implementing governance, security, and quality practices, testing data movement and transformation code, resolving defects and incidents, and advising customers on technical decisions. The role also supports AI, machine learning, business intelligence, and data product adoption while collaborating across teams and educating end users.

Conduct prospect and donor research to support philanthropic fundraising, including wealth assessments, portfolio analysis, donor profiling, and pipeline development. Translate data into actionable recommendations, prepare research reports, collaborate with fundraising and cross-functional teams, and improve donor database and prospect management systems. Monitor philanthropy trends and maintain confidentiality when handling sensitive donor information.