Senior Power BI & Data Analyst

The Senior Power BIData 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 IntelligencePower 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., * 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, RevenueCommercial 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, InventorySupply 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.

FinancialProfitability 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, EngineeringGovernance

• 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 SupportAutomation

• 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., * 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.

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., * 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., Targets are illustrative and should be finalized against organizational baselines and priorities.