Building a Data Lakehouse and Secure Delivery System for Software Artifacts

• Led the architecture and development of a Data Lakehouse platform for enterprise-scale data storage and analysis.
• Designed and launched a secure component analysis (SCA) system to scan, validate, and deliver safe software artifacts.
• Developed a custom ETL pipeline integrated with Greenplum and Airflow, enabling flexible, reliable data processing.
• Mentored three intern cohorts (Python + Go, DevOps, React) and initiated an internal R&D direction for the company.
• Participated in pre-sales and architectural evaluation of new projects, aligning tech decisions with business needs.

• Built a new development department from scratch, defined internal standards, and established the tech strategy.
• Oversaw in-house development that replaced vendor software with no loss of service quality.
• Successfully migrated two mission-critical systems from Oracle to PostgreSQL, significantly reducing licensing costs.
• Re-architected the company’s DWH, moving 70% of reporting to the new platform for improved flexibility and speed.

Over a three-year period, we worked closely with one of Russia's largest banks to successfully establish a robust AI monitoring system. This system operates with precision to swiftly address system failures by pinpointing their root causes and making well-informed decisions. The model expanded the scope of forecasted indicators to a total of 50,000, achieving an 85% reduction in false positive alerts. It also delivered a 20% improvement in precision compared to previous methodologies.

Led a team to develop an AI monitoring system that was capable of detecting anomalies in various business indicators. By closely working with one of Russia's largest banks over a span of 3 years, we designed and implemented an advanced backend system dedicated to the detection of anomalies across a wide spectrum of critical business metrics. This system significantly decreased the time and effort previously invested in manual incident detection and enhanced operational efficiency.

I coordinated a team in the development of two distinct automated incident detection models, which yielded substantial improvements in incident detection times, reducing them by an impressive 5 to 15 minutes. These models played a pivotal role in streamlining processes and optimizing the bank's operations.