- Migrated an existing data architecture (Python, Postgres, bash cron jobs) to a more scalable and efficient one (Scala & Python, k8s, AWS S3, Argo, Postgres), using modern best practices in the data warehousing;- Designed and developed MLOps architecture to take the existing manual model process (MLOps Lvl 1) to the next level with ML and CI/CD pipeline automation (MLOps Lvl 2-3);- Developed ETL processes for aggregating unstructured and structured data from different sources, validating, transforming and loading it to Data Lake and Data Warehouse for analyst's usage;- Integrated data from a wide range of source systems such as micro-services, RDBMs, external APIs, AWS S3, Kafka;- Supported and maintained the data infrastructure of the entire company with more than 5 million active users.

- Worked with different datasources: DB/API/filesystem/web scraping;- Supported data analysts and data scientists for scaling existing models;- Implemented and improved ETL pipelines (Teradata ecosystem, atomic, bash, python);- Helped internal business customers troubleshoot and optimize SQL (Oracle, MySQL) queries and ETL solutions to solve reporting and analytics problems;- Prepared SQL queries for massive parallel processing database (MPP) - Teradata;- Database architecture and data warehousing.

- Perform data munging (acquiring, cleaning, visualization)- Build, train, estimate and improve classical ML models- Conduct exploratory data analysis- Do feature engineering