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Abdullah Inayat

Abdullah Inayat

Data Analyst | Business Intelligence Analyst | Power BI Specialist

Talent
LahoreMember since September 4, 2026
Data AnalysisSQLPower BIDAXData ModelingData VisualizationPower QueryDashboard Development
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Data AnalysisSQLPower BIDAXData Modeling

About

Detail-oriented Data Analyst with hands-on proficiency in SQL, Power BI, Advanced Excel, and Power Query, focused on turning raw multi-source data into clear, decision-ready insights. Experienced in end-to-end dashboard development — data modeling, DAX measures, and Power Query ETL — paired with SQL for querying, filtering, and joining data across tables. Self-driven learner with a visual design background, seeking a Junior Data Analyst / BI Analyst role to support data-driven decision-making.

Experience

Data Analyst at Personal Case Study

Multi-Page Retail Sales & Store Performance Dashboard

  • Cleaned and transformed multi-table retail datasets using Power Query to maintain strict data integrity before modeling.
  • Built a 4-tab Power BI report (Sales Analysis, Order Overview, Return Analysis, Store Analysis) with consistent KPI cards for Sales, Profit, Profit Margin %, and Cost.
  • Identified Store 13 as the top-performing location by sales/profit, while flagging it for high return volumes tied to the "Hermanos" brand to highlight potential inventory quality issues.

Data Analyst at Personal Case Study

Retail Sales Dashboard (Electronics, Clothing & Furniture)

  • Tracked Sales, Profit, Quantity, and Average Sale across product categories, states, and payment modes using custom DAX measures.
  • Built customer-wise and sub-category sales breakdowns with Quarter and State filters for drill-down analysis.
  • Visualized monthly profit trends and payment-mode splits (COD, Credit/Debit Card, EMI) to support channel-level decisions.

Data Analyst at Personal Case Study

Pizza Sales Performance Dashboard

  • Analyzed Total Revenue, Orders, and Units Sold with a Top 10 Best-Selling Items ranking and category-wise sales split.
  • Identified Thursday–Saturday as the peak order window, providing actionable insights for staffing and inventory planning.

Education

BS in Information Technology – Virtual University of Pakistan (2026-01)

Intermediate in Computer Science – GICCL (2023-01 – 2025-12)

Skills

Data AnalystBusiness Intelligence AnalystPower BI Specialist

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