Data Engineer | DevOps Engineer | BI Systems Architect

With over 2.5 years of experience in designing and implementing end-to-end data solutions, I specialize in building scalable ETL/ELT pipelines, optimizing analytical databases (ClickHouse, PostgreSQL), and migrating BI ecosystems. My expertise lies at the intersection of data engineering, infrastructure automation, and business intelligence.

🎓 Currently a 2nd year Master's student at Tyumen Industrial University, majoring in "Neural Network Technologies in Automated Control Systems".

Ретэйл ИТ | Oct 2025 – Present (4 months) | Екатеринбург
Retail & Logistics

• Architected end-to-end ETL/ELT pipelines integrating data from Oracle DWH, Excel/CSV files into ClickHouse, centralizing data and eliminating manual exports
• Reduced report preparation time from several hours to minutes by implementing automated data pipelines
• Optimized ClickHouse database structure with storage engines (MergeTree, ReplacingMergeTree), partitioning, and projections, improving query performance 3-5x
• Led BI migration from Qlik Sense to Apache Superset, maintaining business logic while reducing licensing costs and accelerating new report deployment by 40%
• Built Airflow orchestration system from scratch in docker-compose, implementing DAGs with incremental loading, retry logic, and error handling, achieving 99.9% data delivery reliability
• Developed complex analytical SQL queries using window functions, CTE, and self-JOINs for ClickHouse, forming the foundation for real-time KPI dashboards

ООО "1Т" | Jun 2023 – Aug 2025 (2 years 3 months) | Москва
EdTech & IT Services

• Optimized DWH architecture (PostgreSQL + ClickHouse), reducing aggregate report execution time from 15 minutes to 90 seconds (10x improvement)
• Developed and maintained 15+ Airflow DAGs achieving 99.9% success rate with automated retry, error logging, and Telegram alerts
• Built CI/CD pipeline on GitLab CI automating Docker builds, Kubernetes deployments, DB backups - eliminating 85% of manual operations
• Implemented real-time CDC replication PostgreSQL → Kafka via Debezium, improving data freshness 5x
• Created Python ETL parsers with Pandas, improving raw data processing speed 3x while reducing RAM usage by 40%
• Integrated Hugging Face LLM into data pipeline via FastAPI, reducing NLP request latency from 8 to 1.2 seconds and saving 30% GPU resources
• Automated infrastructure monitoring with Prometheus + Grafana, reducing incident response time from 30 to 5 minutes, achieving 99.97% uptime
• Mentored 7 interns in Data Architecture; 2 hired full-time, 2 received offers from other companies

• 10x faster aggregate reports (15min → 90sec) through DWH optimization
• 3-5x improvement in analytical query performance via ClickHouse optimization
• 40% reduction in new report deployment time after BI migration

• 99.9% data pipeline success rate with robust Airflow DAGs
• 85% reduction in manual operations through CI/CD automation
• 99.97% infrastructure uptime with Prometheus/Grafana monitoring

• Reduced licensing costs by migrating from Qlik Sense to Apache Superset
• 30% GPU resource savings through LLM integration optimization
• 40% RAM reduction in ETL processes via Python optimizations

I build reliable, scalable, and observable data infrastructure that enables businesses to make faster decisions while reducing operational overhead. My solutions are measurable in speed, stability, and resource efficiency.

Open to interviews, technical challenges, and case discussions!