- Developed and maintained scalable ETL pipelines for processing large volumes of data, ensuring high reliability (increased the number of DAGs in Airflow from 4 to 150, processing ~1TB of data daily);- Configured and administered Apache Airflow, including the development of custom operators to automate unique workflows (PostgresExtractor, TelegramOperator);- Optimized complex SQL queries (BigQuery, PostgreSQL);- Led the team of 2 data engineers and 1 analyst, successfully managing increased workload through process optimization and automation;- Solved custom analytical tasks using Python and SQL (result - the creation of a new internal analytical product.);- Optimized Google Cloud expenses, reducing costs by 40%. Employed methods such as partitioning and clustering tables, optimized queries and ETL pipelines.- Developed multiple Kubernetes microservices for data parsing and automated SQL filter creation, optimizing the workflow of data analysts.

- Data extraction tasks in internal and external data sources;- Feature-engineering for Data science tasks;- Created regulatory ETL processes;- Created solutions for internal usage by data scientists (regexp-service for parsing data);- Designing experiments with diverse machine learning models for credit scoring tasks

- Developed and maintained dashboards and reports in Power BI.- Analyzed business processes and recommended optimizations.- Worked with large data sets- Collaborated closely with product management teams to identify analytics needs.