Top Senior Data & Analytics Jobs in Portland, OR

Leads applied AI and health data architecture initiatives for payer clients. Designs cloud-native platforms, pipelines, data models, integrations, and modernization roadmaps across AWS, Azure, Databricks, and Snowflake. Oversees data quality, governance, anonymization, performance optimization, and legacy migrations while advising stakeholders on AI-ready architectures. Manages multi-team delivery, resolves technical issues, presents solution tradeoffs, and coaches practitioners.

Leads advanced analytics, reporting, scalable data models, dashboards, and data pipelines supporting Business and Marketing teams. Partners with senior stakeholders and AI Engineering to implement AI and machine learning workflows, predictive insights, and automation. Develops documentation, drives analytics engineering best practices, and mentors team members. Requires strong SQL, Python, APIs, data modeling, stakeholder communication, project leadership, and blockchain expertise.

Develops enterprise supply chain dashboards, scorecards, reports, data models, and business intelligence solutions using Oracle Fusion Data Intelligence and related platforms. Analyzes complex datasets, creates KPIs, validates data, supports governance, resolves reporting issues, and translates business needs into actionable insights. Partners with procurement, sourcing, inventory, warehouse, and supplier-management stakeholders; provides training and production support; and leads continuous improvement initiatives.

Performs complex risk adjustment and healthcare program analyses using statistical methods and data from multiple sources. Extracts, cleans, aggregates, and queries data; develops dashboards, reports, and presentations; evaluates programs and outcomes; identifies root causes; and recommends solutions. Partners with cross-functional stakeholders, applies healthcare and regulatory knowledge, leads projects, communicates findings to senior leaders, supports strategy and implementation, and mentors junior analysts.

Provides information systems analysis, application development, database administration, technical support, and GIS services for Sheriff's Office operations. Designs, tests, implements, and maintains applications, databases, queries, reports, scripts, and automated processes. Analyzes statistical, business, and spatial data; develops maps, visualizations, and geospatial applications; troubleshoots technical issues; and supports public safety planning, investigations, and resource deployment.

Leads the design, development, and governance of scalable Databricks Lakehouse data products across Azure and AWS. Responsibilities include hands-on coding, code review, batch and streaming pipeline development, cross-cloud integration, data modeling, quality, lineage, security, CI/CD, observability, and technical direction across distributed engineering teams. The role partners with product, architecture, governance, and business stakeholders while supporting analytics, reporting, and AI use cases.

Leads manufacturing analytics and data systems initiatives for a polishing pads factory. Develops Power BI dashboards, KPI scorecards, automated analytical tools, data models, and reporting solutions using cloud platforms and manufacturing systems. Analyzes production and quality data, resolves data-quality and pipeline issues, supports automation improvements, and communicates insights to technical and business stakeholders. Partners cross-functionally on continuous improvement, data governance, digital transformation, and scalable analytics solutions.

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.

Build and maintain large-scale data pipelines and analytics platforms for EDA datacenter observability, telemetry, reliability analysis, and capacity forecasting. Develop batch and streaming workflows, data models, quality frameworks, monitoring, and retention strategies using metrics, logs, traces, and hardware health data. Partner with observability engineers, data scientists, analysts, and infrastructure teams to deliver scalable, reliable, discoverable datasets for analytical and predictive use cases.

Transforms fraud and risk data into actionable insights for detection, investigation, recovery, mitigation, and audit readiness. Builds governed Databricks datasets and pipelines, integrates cross-channel data, analyzes fraud exposure and model performance, develops dashboards, and supports regulatory requests. Partners with Fraud Operations, Risk, Technology, and senior leaders to operationalize vendor machine-learning outputs and improve data quality, governance, and risk decision-making.