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  • Joined Nov 30, 2025

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MaXaM-data/README.md

Hi, I'm Max 👋

Early-career Data Analyst with a background in compliance-heavy government operations.
I like turning messy operational data (SLAs, risks, caseloads, shipping, etc.) into clean, auditable dashboards in Power BI – with a bit of Python when privacy or data cleaning is involved.

  • 🔎 Interested in: operational analytics, Cyber GRC, supply chain / logistics, public sector data
  • 🏛️ Day job: working in the UK public sector on caseloads, risk and compliance
  • 🎓 Studying towards: PL-300 (Power BI Data Analyst)

🔧 Tech & Tools

  • Languages: SQL (joins/filters/aggregations), Python (pandas for cleaning & simple ETL), DAX
  • Analytics & BI: Power BI (Power Query, data modelling, KPIs, time intelligence), Excel
  • Cloud & Governance: Azure Fundamentals (AZ-900), Data Fundamentals (DP-900), APM PFQ
  • Data themes: SLA monitoring, risk scoring, GDPR-aware reporting, star schema modelling

📊 Portfolio Projects

1. Cyber GRC Risk Monitor

Power BI dashboard for tracking vulnerability SLAs, breach rate and critical server risk.

  • Built on a vulnerability scan–style dataset (Kaggle).
  • Star schema with Fact_Findings + dimensions for assets, vulnerabilities, severity, teams and dates.
  • Page 1: Enterprise Vulnerability Risk Monitor – KPIs for open vulns, SLA breach count/rate, critical servers; SLA breaches by department; severity mix.
  • Page 2: Server Vulnerability Details – server-level table (CVSS, OS, days open) with slicers and a treemap of top attack vectors.
  • Designed so row-level security by department can be added for real-world use.

🔗 Repo: Cyber GRC Risk Monitor

2. Global Supply Chain & Logistics Dashboard

End-to-end project combining Python (pandas) for PII anonymisation with Power BI for SLA & margin analysis.

  • Uses the DataCo Supply Chain dataset (~180k orders).
  • Python script to strip customer PII (emails, names, addresses, passwords) and export a GDPR-style anonymised CSV.
  • Star schema in Power BI with Fact_Orders + dimensions for customer, product, geography, shipping mode and date.
  • Page 1 – SLA & Margin Overview
    • KPIs: Total Orders, Total Sales, Total Profit, Late Delivery Risk %, Net Profit Margin, Avg Days to Ship.
    • Late Delivery Risk % by Shipping Mode, Net Profit Margin by Market, SLA risk over time.
  • Page 2 – Commercial Insights & Profit Analysis
    • Shows that Same Day shipping is the least profitable mode globally.
    • Quantifies that in LATAM, late deliveries earn ~7% less profit per order than on-time shipments.
    • Order volume & shipping mode mix by market to give context.

🔗 Repo: Global Supply Chain & Logistics Dashboard

Currently focusing on:

  • Deepening Power BI + DAX (time intelligence, advanced modelling)
  • Using Python + pandas for data cleaning, anonymisation and simple ETL workflows
  • Building more domain-relevant projects around risk, SLAs and public sector operations

📫 Contact

Pinned Loading

  1. cyber-grc-risk-monitor cyber-grc-risk-monitor Public

    Power BI Cyber GRC dashboard for vulnerability SLAs, breach rate and critical server risk.

  2. global-supply-chain-logistics-dashboard global-supply-chain-logistics-dashboard Public

    Power BI + Python project analysing global shipping SLAs, late delivery risk and profit margins.

    Python