Top Entry Level Analysis Reporting Jobs in Portland, OR

Designs, implements, and optimizes Salesforce Data Cloud solutions. Responsibilities include configuring data streams, integrating Salesforce and legacy data with MuleSoft or ETL tools, mapping data objects, creating identity resolution rules, building calculated insights with SQL, developing customer segments, configuring activation targets, and managing data governance, privacy compliance, system performance, storage, and usage.

Build and maintain full-stack data engineering solutions supporting BioIntelliSense’s remote patient monitoring and clinical intelligence platform. The role likely involves processing health-monitoring data, enabling analytics, and supporting reporting for clinicians, patients, and employers. Specific responsibilities and qualifications are not provided in the description.

Build and manage end-to-end marketing data platforms on Google Cloud. Responsibilities include configuring GA4 and Google Tag Manager, creating BigQuery ingestion pipelines, managing Cloud Functions, Pub/Sub, IAM, and service accounts, developing LookML semantic models and dashboards, and orchestrating ETL and reverse ETL workflows. The role also supports CDP integrations, identity resolution, audience syncing, experimentation, data quality assurance, and lifecycle marketing analytics.

Serves as the technical and client-facing link between Inca Digital’s data engine and high-stakes financial and government clients. Conducts OSINT, web scraping, enhanced due diligence, blockchain and crypto investigations; analyzes large datasets; audits and synthesizes technical findings into intelligence reports; briefs executive stakeholders; and coordinates cross-functional teams to align products with client needs.

Leads client-facing database migration and modernization engagements across AWS, relational, and NoSQL platforms. Designs migration architectures, data pipelines, ETL/ELT workflows, proof-of-concepts, and reusable technical accelerators. Facilitates executive conversations and workshops, translates complex technical solutions into business outcomes, and supports pre-sales opportunities. The role also develops GenAI-enabled migration tooling, influences senior stakeholders, and collaborates with distributed engineering, product, and data teams.

Develop and evaluate realistic Excel tasks and solutions for AI training data. Apply advanced Excel capabilities including formulas, PivotTables, data modeling, dashboards, Power Query, Power Pivot, and VBA across multiple industries and business functions. Provide written feedback, close research knowledge gaps, improve model performance, and collaborate with subject matter experts. Requires reliable weekday availability, initially 40 hours per week and 20 hours thereafter.

Organise and curate large amounts of raw material: extract key points from transcripts, collate and maintain clean spreadsheets, tag and categorise links and documents, and create findable information structures following or suggesting systems.

Own production data ingestion pipelines for healthcare and benefits data, including normalization, mapping, validation, reconciliation, observability, and secure handling of sensitive information. Unify census data processes, scale the platform, improve identity matching, and build automation and TypeScript tooling. The role requires strong Python and SQL skills, production orchestration experience, reliable testing and data-quality practices, and effective communication with technical and nontechnical stakeholders.

Manage end-to-end data operations, including collection, processing, delivery, pipeline optimization, and data quality monitoring. Collaborate with engineering, product, and operations teams to improve data workflows, identify inefficiencies, and implement scalable solutions. Analyze data to provide actionable insights for decision-making and AI model development while communicating technical issues to non-technical stakeholders.

Designs and leads enterprise data architecture, including warehouses, lakes, lakehouses, data models, integration pipelines, governance, security, and analytics platforms. Partners with engineering, analytics, application, and business teams to create scalable cloud data ecosystems. Responsibilities include technology evaluation, migration and modernization strategy, architecture reviews, documentation, data quality, lineage, metadata, and technical mentorship.