Tinkoff

Reports, Analysis, SQL, Data Analysis, Python, Superset, BI

R-One

● Starting from scratch the first dashboards for the analytics team. ● Transition from Excel to interactive dashboards. ● Creation of an automated system of daily updates. Business tasks: ● Conducting interviews and collecting requirements from customers (users) of different levels. ● Development of technical specifications for developers / data engineers. ● Development and update of descriptions and instructions on Confluence. Data engineer tasks: ● Writing SQL queries of varying complexity to work with Clickhouse, Oracle databases. ● Processing data in Python. ● Create datamarts. ● Automation of ETL processes in Airflow. BI tasks: ● Building dashboards based on datamarts. ● Data analysis: search for anomalies, trends, growth points.

CityGals Samara

1. Automation of the collection and processing of information in Python and Airflow: The company has completely gone from collecting information "by hand" in Excel tables to automated information collection (using Python and Airflow) and working in interactive Dashboards. 2. Building a model for assessing cities by the quality of their work (based on RFM analysis): The development of a system of metrics for evaluating cities helped to increase profits by 12-14% and reduce the application closing time from 7 to 3-4 days. ● Analysis of key indicators (number of applications, average bill, margin, profit, profitability). ● Development and construction of Dashboards to track company performance in several areas: - Dashboard for CCTV direction. - Dashboard for employees for quick tracking of indicators. - Dashboard for executives to develop a company development strategy. ● Optimization and automation of business processes (writing technical specifications and subsequent testing of the model, creating a model for automating pricing). ● Weekly Ad Hoc reports.