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Data Science with less than a year in analytics, BI, and data warehousing with experience in SQL, Po
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I am a Data Science undergraduate specializing in analytics, BI, and data warehousing. Experienced in designing SQL Server data warehouses, building ETL pipelines with SSIS, developing Power BI dashboards, and applying machine learning models to business problems. Seeking a Data Analyst, BI Analyst, or Data Science internship.
SLIIT
BSc (Hons) Information Technology · Data Science Specialization
August 1, 2023 – June 30, 2027
Unknown
G.C.E. Ordinary Level
N/A – May 31, 2016
Unknown
G.C.E. Advanced Level · Biology Stream
N/A – May 31, 2018
Data Warehouse & Analytics – Medallion Architecture
January 1, 2026 – Present
Designed 3-layer Medallion Architecture (Bronze → Silver → Gold) consolidating ERP and CRM source systems into a unified analytical model. Built ETL pipelines for ingestion, cleansing, standardization, and loading across all three layers; modelled star schema fact and dimension tables optimized for analytical queries. Delivered SQL-based analytics covering customer behavior, product performance, and sales trends; documented full data architecture using Draw.io.
View ProjectSmart Campus Operations Hub
January 1, 2026 – Present
Built full-stack campus management platform with Spring Boot layered REST API and React.js frontend; implemented Google OAuth 2.0 with role-based access (USER / ADMIN / TECHNICIAN). Integrated GitHub Actions CI/CD pipeline for automated build and deployment; clean service/repository/controller architecture with MySQL persistence.
View ProjectCustomer Shopping Behavior Analytics
January 1, 2026 – Present
Cleaned and transformed a 3,900-customer dataset in Python (pandas) covering demographics, purchase history, product categories, and payment behavior. Wrote PostgreSQL queries for aggregation, segmentation, and trend extraction across customer cohorts and seasonal patterns. Delivered interactive Power BI dashboard with slicers tracking revenue trends, category performance, and buying patterns.
View ProjectKlassy T-Shirts Full-Stack E-Commerce Platform
January 1, 2026 – Present
Built full MERN stack e-commerce platform with JWT authentication, RESTful APIs, and role-based access control across customer and admin dashboards. Implemented real-time order tracking, product management, and cart functionality with persistent MongoDB data layer.
View ProjectEnd-to-End Data Warehouse & BI Solution – Global Superstore
January 1, 2026 – Present
Architected a Star Schema data warehouse in SQL Server integrating 3 heterogeneous sources (CSV, Excel, Text) across a 51,291-row dataset with structured error handling. Built end-to-end SSIS ETL pipelines covering staging, transformation, data validation, and SCD Type 2 to preserve full customer history. Implemented SSAS Multidimensional Cube with hierarchies and calculated measures enabling full OLAP analysis (Drill-down, Roll-up, Slice, Dice, Pivot) via Excel PivotTables. Published interactive Power BI dashboard with drill-through navigation and cascading filters for sales, product, and customer reporting.
View ProjectStatistical Modelling – Data-Driven Decision Making & Organizational Confidence
January 1, 2026 – Present
Developed a synthetic dataset of 1,000+ companies across 18 variables, constructed following PwC Global Data & Analytics Survey and McKinsey Analytics Maturity Model frameworks. Conducted full-spectrum analytics - Descriptive, Inferential (ANOVA, t-tests, Pearson correlation r = +0.954), and Predictive – entirely in R. Built and compared 5 ML models: MLR, Ridge Regression, Decision Tree, Random Forest, Gradient Boosting – recommended MLR with Test R2 = 0.91, MAE = 5.4pp, 5-fold CV R2 = 0.917 ± 0.008. ANOVA confirmed data maturity drives strategic confidence: Level 5 firms averaged 99.3% vs 29.9% at Level 1 (F = 1059, p < 0.001) – a 69-point gap.
View ProjectMeta Ad Performance Analysis Dashboard
January 1, 2026 – Present
Analyzed a simulated dataset of 216,000 impressions, 25,400 clicks, and 1,300 purchases across the full marketing funnel – demographic, geographic, and time-based dimensions. Achieved 11.76% CTR with 13.56% engagement rate; identified bottom-of-funnel drop-off between engagement and purchase conversion. Segmented audiences revealing females aged 18-30 drive 43% of engagement; identified Video and Stories as highest-ROI ad formats. Recommended geographic budget split: India/Brazil (volume) vs Germany/UK (value) based on DAX measures and demographic conversion analysis.
View ProjectMongoDB Data Modeling Path
MongoDB University
June 1, 2026 – Present
AI-ML Stage 1
SLIIT
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects showcase a diverse range of technical skills relevant to data science, including data warehousing, BI, and machine learning. The inclusion of full-stack development projects (Smart Campus Operations Hub, Klassy T-Shirts) indicates a broad technical curiosity and willingness to explore different domains, which can be a positive cultural fit for dynamic environments. The target role of 'Data Science' aligns well with the specialization and project focus. The academic nature of all projects means real-world, collaborative, or corporate cultural fit aspects are not directly observable from the provided data.
Soft Skills & Operational Fit
The candidate demonstrates a project-oriented approach, indicating self-motivation and the ability to work independently on complex tasks. The detailed project descriptions suggest good organizational skills and a structured approach to problem-solving. While direct experience in team collaboration or stress handling is not explicitly detailed, the breadth of academic projects implies a capacity for managing multiple priorities. The candidate's academic background and project work align well with roles requiring analytical thinking and data-driven decision-making.