Data Analysis · Business Intelligence

Mohammed Ashraf Hassan

Data & BI Analyst

I turn messy business data into Power BI dashboards that tell owners and managers exactly where the money is going. Power BI Services Consultant

Cairo, Egypt · Available for remote freelance work

3
End-to-end BI projects
300K+
Records modelled
10
Dimension tables designed
7
Core analytics tools

01 — About me

An analyst who starts with your decision, not your data

I'm a Data & BI Analyst who builds the reporting layer that business owners, sales managers, and stakeholders actually use to make decisions. My work starts where most reports fail: with the raw file. I clean it, model it into a proper star schema, define the KPIs that matter, and then design a dashboard that answers a question in one glance instead of five clicks. My background is in Electronics & Communication Engineering, which trained me to break messy systems into measurable parts — the same instinct I now apply to sales, operations, and pricing data. Through the DEPI Junior Data Analysis Track I built three full analytics projects end to end, covering retail performance, flight pricing, and store sales. I'm straightforward to work with: I ask what decision you need to make, then build the shortest path to it. No 40-page decks, no vanity charts.

  • Clear KPI definitions agreed before a single chart is built
  • Star-schema models so numbers never double-count
  • Reports designed for one-glance answers on desktop and mobile
  • Documented work you can hand to the next person

02 — Education

Training and background

B.Sc. Electronics & Communication Engineering

Institute of Aviation Engineering and Technology (IAET)

Engineering degree with a strong quantitative and systems-analysis core.

Junior Data Analysis Track

Digital Egypt Pioneers Initiative (DEPI)

Applied training in SQL, Excel, Power Query, data modelling, and Power BI.

03 — Skills

The toolkit behind every report

Analysis & Insight

  • Data Analysis
  • KPI Definition
  • Trend & Variance Analysis
  • Root-Cause Breakdown

Visualization & Reporting

  • Power BI
  • Data Visualization
  • Dashboard Design
  • Executive Reporting

Data Preparation

  • Data Cleaning
  • Power Query
  • Excel
  • Star-Schema Data Modelling

Querying

  • SQL
  • Joins & Aggregations
  • DAX Measures

04 — Work experience

Where the hands-on work comes from

Freelance Data & BI Analyst — Independent
Present
  • Build Power BI dashboards and clean, modelled datasets for business owners and sales teams.
  • Take projects from raw CSV to a decision-ready report, including KPI definitions and documentation.
Junior Data Analysis Track — Trainee — Digital Egypt Pioneers Initiative (DEPI)
Training Programme
  • Completed a structured analytics track covering SQL, Excel, Power Query, data modelling, and Power BI.
  • Delivered the Retail Performance & Operations Dashboard as the capstone project, guided by Eng. Moataz Badran with advisory support from Hassan Ashraf.

05 — Projects

Selected builds, from raw file to decision

Each project shows the report pages and the data model underneath. Tap any image to view it full size.

Lead project · DEPI capstone

Retail Performance & Operations Dashboard

4.34M
Total sales
29.62%
Net profit margin
14.80%
Cancellation rate
625.61K
Lost to cancellations

The question

A retail business could see its revenue but not what was quietly draining it — cancelled orders, slow shipping, and discount-heavy sales reps.

What I built

A three-page Power BI report over a cleaned RetailSales dataset, modelled as a star schema joined on surrogate keys: an Executive page for revenue and margin, an Operations page for fulfilment health, and a Sales Force page for rep and product performance. All pages share date, channel, brand, and category filters.

What it revealed

29.62% net profit margin on 4.34M in sales, with margin dipping to 26.15% in Q3 — a seasonal squeeze worth planning around.
14.80% of orders were cancelled, equal to 625.61K in lost revenue: the single biggest recoverable number in the business.
Average shipping lag of 3.92 days showed no strong link to cancellations by city, ruling out delivery speed as the main cause.
Product-level margin flags exposed items selling in volume while earning below-target margin, and discount rates varying by rep up to 9%.
Power BI Power Query DAX Star Schema Excel

Live report

This report opens in its own tab.

Report pages

Retail executive overview dashboard showing total sales, net profit margin trend, profit by category and profit by sales channel
Executive Overview — revenue, margin trend, category and channel profit
Retail operations dashboard showing order cancellation rate, lost revenue, average shipping lag and cancellation rate by city
Operations Overview — cancellations, lost revenue and shipping lag
Retail sales force dashboard showing order volume versus discount rate by sales person, payment method split and product margin table
Sales Force Overview — rep performance, discounts and product margin flags
Documentation page listing the project dataset, star schema description and KPI definitions
Documentation page — dataset, schema and KPI definitions

Data model

Power BI star schema with a central fact table joined to customer, segment, product, brand, geography, date, order status, payment method and sales channel dimensions
Star schema — one fact table, ten dimensions, joined on surrogate keys
Executive Dashboard / Business Intelligence

Superstore Analytics

$2.29M+
Total Sales Revenue
$286K+
Net Profit Contribution
12.5%
Overall Profit Margin
9K+
Records Analyzed

Designed and developed a high-end, multi-page "Clean Dark" Executive Dashboard for retail performance tracking using Power BI and a robust star schema data model. Built to prioritize rapid executive digestion, the portal features a dynamic home landing page with custom HTML/CSS alert cards, transparent button navigation overlays, and real-time KPI monitoring. Key findings uncovered critical operational insights: while product categories like Phones and Copiers drive strong revenue, specific sub-categories like Tables and Bookcases incur heavy net losses, and aggressive discounting past 20% severely erodes profit margins below zero. The dashboard also highlights customer segmentation trends—where the Consumer segment commands 46.8% of total profit—and isolates key account risks to drive immediate executive action.

Power BI DAX HTML / CSS Data Modeling (Star Schema)

Live report

Open report

Report pages

Data model

Pricing & route analytics

Flight Tracking DB

300.2K
Flights analysed
20.89K
Avg ticket price
12.22
Avg duration (hrs)
6bn
Total ticket value

The question

With 300K flight records, which airlines, cabin classes, and booking windows actually drive ticket price — and where does price stop being about distance?

What I built

A three-page Power BI report on a star-schema model with airline, city, class, departure-time, and arrival-time dimensions. Pages cover an executive summary, a pricing and booking deep dive with correlation analysis, and a route-level view.

What it revealed

Business class is 11.12% of tickets sold but a far higher share of value, with average business fares several times economy.
Duration barely predicts price: the correlation plot flattens after the first few hours, so scheduling — not distance — sets the fare.
Fares stay flat until roughly 10 days before departure, then spike sharply — a clear last-minute pricing window.
Vistara and Air India dominate both flight volume and premium pricing; the six busiest city pairs cluster around Chennai, Bangalore, and Kolkata.
Power BI Power Query DAX Star Schema

Report pages

Flight Tracking DB landing page with navigation to executive overview, pricing and booking, and route analysis pages
Report landing page with guided navigation
Flight executive overview dashboard showing average ticket price, flight volume, trips per airline and ticket share by class
Executive Overview — volume, price and class share
Flight pricing dashboard showing duration versus cost correlation, cost variance by stops and class, booking window price trend and time-of-day price matrix
Pricing & Booking — correlation, booking window and time-of-day pricing
Flight route analysis dashboard showing average travel duration by route, top five city trip pairs and flight counts by source city and stops
Route Analysis — durations, top city pairs and stop mix

Data model

Power BI star schema with a flight fact table joined to airline, arrival time, city, class and departure time dimensions
Star schema — flight fact table with five lookup dimensions
Sales & profitability

Super Store Sales Analysis

2.90M
Total sales
360.13K
Total profit
47K
Quantity sold
12.42K
Total purchases

The question

Across four years of orders, which segments, categories, and states actually carry profit — and is the trend improving or just noisier?

What I built

A single-page executive sales dashboard over a star-schema model with customer, product, geography, date, and ship-mode dimensions, filterable by year, region, and shipping method.

What it revealed

2.90M in sales produced 360.13K in profit, with quarterly profit growing from 4K to a 62K peak in Q3 2019.
The Consumer segment drives 53% of sales (1.55M), while Home Office contributes only 17% — a clear targeting signal.
Technology leads category sales, with Office Supplies close behind on volume but thinner returns.
Sales concentrate heavily in California and New York, exposing a geographic dependency worth diversifying.
Power BI Power Query Excel Star Schema

Document

Report pages

Super Store sales dashboard showing total sales and profit by quarter, sales by segment, sales by category and sales per state map
Sales & Profit overview with year, region and ship-mode filters

Data model

Power BI star schema with a sales fact table joined to customers, products, geography, date and ship mode dimensions
Star schema — sales fact table with five dimensions

Let's work together

Tell me the decision you're stuck on. I'll build the dashboard that answers it.

Send over your data file or a short description of your reporting problem. You'll get a plain answer on scope, timeline, and what the finished report will show — no obligation.