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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Power BI & Data Analyst - **Company:** The Job Network - **Location:** Eugene, OR, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Microsoft Excel, Data Analysis, Microsoft Azure, Big Data, Cluster Analysis, Data Architecture, Data Governance, Data Integrity, Extract Transform Load (ETL), Data Presentation, Data Warehousing, Relational Databases, Decision Support Systems, Statistical Hypothesis Testing, Python (Programming Language), Microsoft Dynamics, SQL Azure, NumPy, Oracle (Applications), Performance Tuning, Query Optimization, Power BI, Salesforce.Com, SAP (Applications), SciPy, SQL Databases, Azure Data Factory, Sql Optimization, Snowflake, Model Validation, Git, Powerquery, Pandas, Microsoft Fabric, Scikit Learn, Information Technology, Statistics Packages, Data Analytics, Machine Learning Operations, Semantic Modeling, Databricks - **Published:** November 13, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-power-bi-data-analyst-eugene-or--41d3ee75-e6b8-4200-94a3-ccf156428f37 ## About the Role Power BI, Data Analysis, Statistical Modeling, Business Intelligence, Telecommunications, Data Analysis, Profit & Loss, Power BI, Predictive Modeling, Forecasting, Bachelor''s degree in Statistics, Mathematics, Data Science, Computer Science, Finance, Economics or related quantitative field; master''s preferred. 7+ years in analytics or BI, including 5+ years of advanced Power BI and 3+ years of statistical modeling, forecasting or predictive analytics. Experience with large datasets, executive reporting and ERP/CRM systems required. Distribution, telecommunications or digital infrastructure experience preferred. Required Technical Competencies * Advanced Power BI, DAX, Power Query (M), semantic modeling, star schemas, security and performance tuning. * Advanced SQL, joins, CTEs, window functions and query optimization. * Python or R with statistical libraries such as pandas, NumPy, SciPy, statsmodels or scikit-learn. * Regression, hypothesis testing, time-series forecasting, clustering, model validation and statistical communication. * Advanced Excel, ETL/ELT, relational databases and data warehousing. Preferred Technologies * Microsoft Fabric, Azure SQL, Azure Data Factory and related Azure services. * Snowflake, Databricks, Dynamics 365, Salesforce, SAP or Oracle. * Git, machine learning workflows and AI-assisted analytics. Professional Competencies * Business and financial acumen; analytical judgment and critical thinking. * Executive communication, stakeholder collaboration and concise data storytelling. * Attention to data integrity, ownership, prioritization and mentoring. ## Description The Senior Power BI & Data Analyst transforms complex commercial, operational and financial data into actionable insights that support strategic decisions, revenue growth, profitability and operational efficiency. This role combines enterprise Power BI development with statistical modeling, predictive analytics, forecasting, data governance and executive-level business partnering., Business Intelligence & Power BI * Design and maintain enterprise Power BI dashboards, reports and semantic models. * Build advanced DAX measures, Power Query transformations and dimensional models. * Integrate ERP, CRM, finance and supply chain data; implement row-level security and refresh monitoring. * Optimize report performance, standardize KPI definitions and enable governed self-service analytics. Statistical Modeling & Advanced Analytics * Apply regression, hypothesis testing, multivariate analysis, segmentation and clustering. * Build time-series and predictive models for revenue, customer demand, inventory and profitability. * Use Python or R to evaluate assumptions, confidence intervals, model accuracy and limitations. * Conduct sensitivity, scenario and what-if analyses and translate results into business actions. Sales, Revenue & Commercial Analytics * Analyze direct and indirect channel performance, bookings, pipeline, gross margin and retention. * Develop sales forecasts, customer profitability and lifetime-value models. * Evaluate pricing elasticity, discounting, margin leakage and cross-sell opportunities. Procurement, Inventory & Supply Chain * Model inventory demand, turnover, stockouts, obsolete stock and working capital. * Develop supplier scorecards, purchasing trends and fulfillment performance analysis. * Identify opportunities to improve procurement efficiency and inventory positioning. Financial & Profitability Analytics * Analyze revenue, gross profit, expenses and budget-to-actual variances. * Develop profitability reporting by customer, product, supplier, channel and business unit. * Partner with Finance on reconciliation, forecasts and scenario planning. Data Quality, Engineering & Governance * Write complex SQL and support ETL/ELT, data warehouse and lakehouse integrations. * Implement validation, reconciliation, documentation and master-data quality controls. * Collaborate with IT on reliable, secure and scalable data architecture. Executive Decision Support & Automation * Present analytical findings, risks and recommendations to executive stakeholders. * Automate repetitive reporting and build reusable models and templates. * Mentor analysts and business users on Power BI and analytical best practices., Reporting Delivery 95%+ on-time scheduled reports Data Accuracy 99%+ accuracy of validated critical metrics Dashboard Performance 95%+ meet agreed performance standards Forecast Accuracy Meet use-case-specific accuracy thresholds Reporting Automation 25-40% reduction in manual reporting effort Analytics Adoption Growth in active dashboard usage Project Delivery 90%+ completed to agreed schedule Stakeholder Satisfaction 90%+ positive satisfaction Model Quality Meet defined validation and stability criteria Targets are illustrative and should be finalized against organizational baselines and priorities. Expected Deliverables * Executive performance dashboards and standardized KPI reporting. * Sales, revenue, customer demand and inventory forecasting models. * Customer, product, channel and supplier profitability analytics. * Pricing and margin optimization analysis. * Documented data definitions, statistical methods, model validations and automated reporting solutions.