Top Senior Analysis Reporting Jobs in Portland, OR

The Lead, Advanced Analytics, Acquisition will analyze data, define metrics, conduct A/B tests, and collaborate with teams to drive growth and insights.

Lead the design of DataOps platforms, focusing on scalable data infrastructures and automation frameworks. Oversee robust data orchestration and establish quality frameworks.

The Staff Data Scientist will lead analytical strategies for Firefox, focusing on product growth, measurement frameworks, and mentoring data scientists while guiding data-driven decisions.

The role involves developing forecast models for oncology products, collaborating with teams, and communicating assumptions for strategic decision-making across the organization.

Support business partners with advanced data insights and subject-matter expertise. Build and maintain reporting pipelines and dashboards, write advanced SQL, perform cohort and forecasting analyses, and deliver recommendations. Partner with stakeholders, run trainings to enable self-serve analytics, and manage projects across teams to measure customer activation, engagement, and product success.

Lead strategic analytics and partner with senior leadership to drive growth, margin, and retention initiatives. Build interactive reporting (Python/Streamlit, BigQuery-connected Google Sheets), perform deep EDA in Jupyter, model and clean data with SQL and dbt, integrate secure AI-assisted tools and automation, ensure analytical quality, and translate insights into measurable business outcomes.

Design and deliver reporting, dashboards, and analytics for higher education advancement and constituent data. Gather requirements, define KPIs, perform SQL-based analysis and data validation, and build actionable Tableau visualizations. Advise clients, run workshops, collaborate with data engineering/CRM teams, document business rules and tests, and support adoption and analytics modernization initiatives within a consulting environment.

Lead technical design and delivery of Databricks lakehouse solutions: gather requirements, design and build scalable pipelines with Spark/PySpark and Python, integrate with cloud, enforce governance, optimize performance, lead code/review/testing/CI-CD, mentor engineers, and liaise with clients.

Design, deploy, and maintain scalable data infrastructure and orchestration for analytics and ML; write optimized SQL over large warehouses; partner with engineering and product to structure source systems; maintain CDC pipelines; scale reporting and expose analytical datasets enterprise-wide while ensuring data management, security, and compliance.

Design and build large-scale data migration systems, map heterogeneous legacy sources into the platform, ensure end-to-end data correctness, create repeatable tooling, support analytics and BI, optimize performance and storage, debug production data issues, and collaborate with Product, Implementation, and Ops for agency onboarding.