N*****i

Improved database performance by optimising SQL queries through indexing, query restructuring, and partitioning techniques, significantly reducing query response time and enhancing overall data retrieval efficiency. Executed comprehensive data cleaning and transformation using Python (Pandas, NumPy) and SQL, removing duplicates, standardising data formats, and preparing datasets for advanced data analysis in Jupyter Notebook and Power BI. Developed end-to-end data integration workflows, connecting structured datasets to Power BI via SQL connectors and APIs. Built calculated measures using DAX, including Weighted Average Price (WAP) metrics, enabling SKU and competitor pricing comparisons and delivering valuable business intelligence. Designed and maintained interactive Power BI dashboards using advanced visualisations (e.g., bar charts, line graphs, heatmaps, slicers), improving data storytelling and supporting KPI tracking—resulting in a 20% boost in client engagement and 25% faster data analysis. Provided ongoing stakeholder support and troubleshooting using Python and SQL, reducing downtime by 13% through proactive resolution of data pipeline issues.

Designed and developed responsive online surveys using HTML, CSS, and JavaScript to collect essential market research data, ensuring cross-platform compatibility and a consistent user experience. Implemented dynamic survey logic and branching with JavaScript to personalise question flow based on user input, improving data relevance and respondent engagement. Applied form validation and error-handling techniques using JavaScript to ensure data accuracy and reduce inconsistencies in survey responses. Conducted data extraction and analysis on collected responses to identify insights, trends, and patterns, generating reports to support marketing strategies and business decision-making.