Turning messy datasets into revenue-driving decisions — through SQL precision, Python rigor, and business-first thinking.
Clarity from complexity — one dataset at a time
I am a Data Analyst with a strong foundation in Software Engineering (currently in my 4th semester at Virtual University of Pakistan). My passion lies in solving real-world business problems by transforming complex, unstructured datasets into clear, actionable narratives.
Rather than just writing code, I focus on business impact. During my internship at DevelopersHub Corporation, I engineered automated data pipelines and interactive BI dashboards that replaced static Excel reporting. With advanced proficiency in SQL, Python (Pandas), and Data Visualization, I specialize in uncovering trends regarding revenue, customer churn, and operational efficiency — helping stakeholders make data-driven decisions confidently.
Case Studies
Certifications
Internship
End-to-end analytical work demonstrating real business impact
Engineered an enterprise-grade Executive Sales Dashboard in Power BI. Features dynamic YoY time-intelligence (DAX), cross-filtering by region/category, and a modern, high-contrast UI tailored for executive boardroom presentations.
Conducted deep exploratory data analysis on 113K+ Brazilian e-commerce orders. Merged 5 relational tables to map delivery delays to customer satisfaction and performed RFM customer segmentation.
Engineered advanced SQL queries (CTEs, Window Functions, Multi-table Joins) to analyze retail databases. Extracted actionable insights on revenue distribution, inventory stockouts, and customer segmentation.
Analyzed £17M+ across 1M+ UK retail transactions using Power Query ETL and a custom Power Pivot relational model to surface at-risk lifetime value.
Engineered an end-to-end Python and Power BI pipeline, utilizing dimensional modeling to pinpoint operational segments driving subscription cancellations.
I turn messy operational data into clear financial directives. No vanity metrics — just specific insights that protect recurring revenue and improve margins.
Identifying exact revenue at risk and mapping the specific lifecycle stages where users drop off, allowing you to trigger targeted retention interventions before the renewal cliff.
Segmenting user bases to isolate the top 20% of customers driving your revenue, enabling marketing teams to optimize acquisition spend on high-LTV lookalikes.
Exposing hidden margin destruction caused by discounting strategies and inventory mismanagement, ensuring top-line sales growth actually translates to bottom-line profit.
Every engagement follows the same rigorous framework — from raw question to boardroom-ready recommendation.
Translate a vague business pain into a precise, measurable analytical question.
Problem StatementETL pipelines, deduplication, outlier handling — building a trustworthy single source of truth.
Clean DatasetSQL joins, Python aggregations, RFM scoring, cohort breakdowns — digging until a signal emerges.
Key FindingsPower BI dashboards and Seaborn charts designed for a non-technical executive audience.
Live DashboardEvery output ends with a £/$ figure — a specific revenue impact, not a vague "consider improving."
Revenue ImpactThe last step is non-negotiable. Any analysis that doesn't end with a quantified business recommendation is just a data exercise — not a decision tool. Every case study in my portfolio closes with a specific £ or $ figure for this reason.
Recognized credentials from leading global institutions
IBM · Coursera
IBM · Coursera
IBM · Coursera
Google · Coursera
A snapshot of my skills, education, and experience
SQL & Querying
Data Visualization
Pandas & EDA
Software Engineering
Last updated: March 2026
Sharing analytical case studies and data-driven insights from real projects.
A complete Python analytics case study on the Olist dataset — covering data engineering, memory optimization, and RFM segmentation to diagnose the true cause of a 97% churn rate and propose targeted business interventions.
Read on Medium
A breakdown of how I processed over 1 million retail transactions using Power Query and built a custom RFM segmentation model to uncover a £390K+ incremental revenue opportunity.
Read on MediumOpen to data analyst roles, freelance projects, and collaborations
Whether you need to turn raw data into strategic insights, build an automated BI dashboard, or tackle a challenging analytics problem — I'd love to hear from you.
