Harshini .v

Business & Data Analyst | Power BI, Excel & Financial Reporting

Talent

Newcastle Upon TyneMember since June 30, 2026

Power BIKPI visualisationStatistical modellingPredictive modellingPythonPandasSQLMySQLOracleData migrationSystem integrationJavaScikit-learnRandom ForestXGBoost

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Power BIKPI visualisationStatistical modellingPredictive modellingPython

About

Data-literate analyst with hands-on experience building Power BI dashboards, automating data pipelines, and translating raw operational data into decision-ready insight.

Background spans enterprise software delivery, ETL/data migration work, and statistical modelling (MSc Data Science, 94.5% model accuracy). Comfortable learning new business systems quickly and presenting findings clearly to non-technical stakeholders.

Experience

Java Full Stack Developer - Cognizant Technology Solutions

(2022-01 - 2023-12)

Extracted, transformed and migrated data across legacy and modern systems via Python ETL pipelines, processing 10,000+ records per day with high accuracy.
Built and maintained backend systems and reporting interfaces supporting operational decision-making for a major retail client with 50,000+ monthly active users.
Worked across 8-person cross-functional Agile team, gathering requirements from business stakeholders and translating them into technical and reporting deliverables.
Queried and manipulated relational data (MySQL, Oracle) to support business reporting and system integration during platform modernisation.
Mentored junior team members and communicated technical findings to non-technical colleagues, demonstrating strong written and verbal presentation skills.

Business Intelligence & Analytics Projects - Independent / Academic Projects

(2023-01 - 2024-12)

Designed and built an interactive Power BI sales analytics dashboard, visualising KPIs and trends to support data-driven business decisions for an e-commerce dataset.
Built automated data-cleaning and pre-processing pipelines in Pandas, cutting data preparation time by 60% — directly applicable to fast, accurate management reporting.
Developed and tuned predictive models (Random Forest, XGBoost) achieving 0.91 AUC, demonstrating strong numerical and analytical rigour applied to forecasting-style problems.