I'm Yasmin, a Microsoft-certified Business Intelligence Consultant with 9+ years of experience across the Middle East and Europe. Based in Dubai, I specialize in Power BI, DAX, SQL, and ETL pipelines that give organizations a single source of truth they can trust.
I've delivered end-to-end BI solutions for clients including Dubai Integrated Economic Zones, the National Bank of Egypt, and Danone, across Finance, HR, Sales, Procurement, and Agriculture. I manage every project from requirements to delivery, replacing spreadsheet chaos and manual reconciliation with scalable, automated reporting that leadership relies on.
Core skills: Power BI | DAX | SQL | Power Query | ETL | Snowflake | Azure | Databricks | Power Automate | Tableau | OBIEE | SAP Business Objects | Excel
Designed and implemented an end-to-end BI solution for financial reporting:
Data Integration: Extracted raw financial data from QuickBooks using Python and loaded it into Azure SQL.
Data Modeling: Performed transformations and built a star schema in Azure SQL to optimize query performance.
Reporting & Analytics: Connected Power BI to Azure SQL views to create executive dashboards with dynamic time intelligence (Selected Month, QTD, YTD) and benchmark comparisons against Last Year, Last Month, and Budget.
AI-Powered Insights: Integrated OpenAI via Python to generate automated narrative insights from financial data, providing executives with actionable commentary alongside visual analytics.
Interactive sales dashboard tracking Amount (TRY) and Volume (KG) by product category and channel, with actual-vs-budget comparisons, price-per-KG trends, and monthly performance.
This Power BI solution delivers end-to-end sales and payment visibility for Rora Orchards across its fruit and blueberry operations, covering both consignment and bin-based sales channels.
Data Architecture
Sales data is sourced from an on-premises SQL Server database via an On-Premises Data Gateway. A Power Automate pipeline captures payment files from email into SharePoint, which Power BI then consolidates with SQL Server into a single reporting layer.
Dashboards & KPIs
The solution includes four report pages:
Consignment Dashboard — tracks STD Cartons Sold , Total Payment, KG Sold , R/Carton , and R/KG , with paid vs. not-paid breakdowns and trend lines across the season.
Bins Dashboard — monitors #Bins Sold , Total Payment , Tons Sold , R/Bin , and R/Ton , segmented by agent, farm, commodity, variety, and packing method.
Sales Detail Table — a flexible drill-through view showing STD Cartons and R/KG by commodity and variety, with a dynamic dimension selector allowing users to pivot the breakdown by Agent, Farm, Commodity, Variety, Export/Local, or Size — and a measure selector to switch between multiple KPIs simultaneously.
Future Payments Analysis — Outstanding payments totaling, broken down by commodity and agent, with ablity to see payment by week,month and year.
All dashboards include slicers for Season, Agent, Farm, Commodity, Variety, and Export/Local class, enabling users to dynamically filter every visual and KPI.
This Power BI solution provides end-to-end operational visibility into the blueberry picking season, covering productivity, quality, wages, and environmental conditions across multiple farms and varieties.
Data Architecture
Picking data comes from an on-premises SQL Server database via an On-Premises Data Gateway, budget data from Excel files on SharePoint for actual-vs-budget comparisons, and live weather data from an external API to correlate conditions with berry quality.
Dashboards & KPIs
The solution includes five report pages:
Picking Analysis — tracks Hectares Planted, Picking KG (Actual vs Budget), Picking Wages (Actual vs Budget), and Picking R/KG across farms, varieties, grades, and orchards. Budget variance indicators show performance above or below target at a glance, with breakdowns by grade (Class 1, Waste, Frozen) and variety.
Picking vs Consignment Analysis — bridges operational and commercial data by comparing Picking KG against Consignment KG per variety, with weekly/monthly trend views and a consignment class breakdown (Export, Local, Frozen, Juice). Variance indicators immediately flag where picking volume isn't converting to consignment as expected.
Picking Wages Analysis — a smart narrative summary auto-generates key highlights including peak wage weeks, highest picking hours, average weekly wages, and average R/Hour. Wages are broken down by type (Contractor vs Employees) and by company, with a full weekly trend across the season.
Sizeband Analysis — monitors berry size distribution across six size bands (under 12mm through 20mm+), with Average Weight and Average Durafel score per variety. Views switch between daily and monthly trends, and a variety tab bar allows quick comparison across all blueberry cultivars.
Durafel & Weather Analysis — correlates berry firmness (Durafel score) and average berry weight against live weather data including Max/Min Temperature and Rainfall. The Durafel vs Standard Deviation chart highlights consistency and quality risk periods, while the daily detail table layers all metrics for precise operational review.
All dashboards include slicers for Season, Farm, Variety, Orchard, Grade, and Month, giving users full control over the scope of analysis across every page.
Tracks headcount, new joiners, turnover, average tenure, age, and Emiratization across business units, with employment-type and gender breakdowns and year-over-year trends.
Monitors the recruitment funnel from applied to hired, with days-to-hire and days-to-join metrics, requisition trends, hiring-source breakdowns, and recruiter performance.
Tracks HR service tickets across business units, including average resolution time, on-time closure rate, satisfaction score, and open, delayed, and closed volumes.
Summarizes procurement activity and savings, including purchase orders, contract value, savings amount, and direct purchases, by business unit, department, and year.
Presents the full income statement, from revenue and COGS to gross profit and net profit, with actual-vs-last-year and budget comparisons, monthly profit trends, and revenue breakdowns by stream and business unit.