● Created a Power BI dashboard to track daily article views on the Mohirdev website, showcasing total views per article and daily view trends for performance analysis. ● Transformed the data in BigQuery Datawarehouse using DBT (Data Build Tool). I created Date models for data that is updated once a week in a separate account, and set it to update weekly. This reduced BigQuery costs by 20%. ● Collaborated with a team to collect IT job posting data through web scraping using Python. Leveraged this data to develop the IT-Market Analysis 2024 dashboard, which was presented to the World Bank for industry insights. ● Collaborated with teams from Sales and Management departments to fulfill their requests for tailored user lists. Extract and deliver the required data by crafting and optimizing SQL queries in BigQuery(CTEs, Subqueries, Joins) ● Developed and optimized Python scripts to transform account turnover data from two of Uzbekistan's largest banks into a format compatible with a financial analysis platform. These fully completed solutions streamline the process, saving accountants up to 1 hour per week and significantly improving efficiency. ● Quickly developed a dashboard in Apache Superset for an outsourced project, contributing to the successful completion of the project's third phase. ● Created physical tables for charts initially built using virtual tables in Apache Superset and loaded the data into ClickHouse using Airflow. This optimization doubled the dashboard's performance speed.

● Automated weekly and monthly reports on equipment performance. Got the data from the SAP HANA database into Power BI via SQL queries. Manipulated data with Power Query and DAX formulas. ● Visualized the monthly sales of SAG Express stores in Power BI. As a result, business decisions are made as a result of monitoring the sales volume of cities, carpet sizes, trends in collections. ● Automated weekly storage material reporting by replacing manual Excel processes with Python's Pandas library, reducing task time from 1 hour to 4–5 minutes. ● Analyzed the reasons for stopping carpet production equipment and divided them into groups in Excel. Found that an average of 20%-35% of outages could be prevented by analyzing the reasons assigned to groups.

● A Model that automatically calculates the payment schedule in Excel has been created for our customers who purchase goods on the basis of installment payments. ● In order to reduce costs, Analyzed all the Company's costs and identified excess costs.