Backend architecture · Data engineering · Distributed workflows
I build Python backend systems and data pipelines for data-intensive products, internal platforms, and model monitoring. My work connects API and database design with asynchronous processing, orchestration, performance, and reliability.
I take ownership from architecture and implementation through code review, releases, observability, and incident follow-up. As a technical lead, I turn business requirements into clear technical plans and help teams deliver systems that are maintainable and safe to change.
- Backend engineering: API contracts, service boundaries, PostgreSQL data models, caching, integrations, and background jobs.
- Data engineering: Airflow orchestration, Spark processing, monitoring and validation workflows, and reporting pipelines.
- Technical leadership: architecture decisions, task decomposition, code review, delivery coordination, and performance improvements backed by measurement.
Explore my projects · Professional experience · Technical toolkit
- Led delivery for up to 15 specialists and owned the technical direction of 12+ backend services.
- Worked with platforms covering 500+ ML models and 50+ test types.
- Improved data-heavy operations by up to 30× and reduced one database workflow from 5 minutes to 10 seconds.
- Increased critical-scenario test coverage to 85%+, reduced production defects by approximately 40%, and improved service response time by 15%.
Company names, internal product names, and proprietary implementation details are intentionally omitted due to NDA restrictions. The public summary focuses on transferable engineering scope and measurable outcomes:
- Designed and developed backend services for data registries, monitoring, validation, reporting, and ML-enabled workflows.
- Built REST and gRPC integrations, event-driven services, asynchronous submit/poll flows, and background processing pipelines.
- Led architecture, task decomposition, code review, release coordination, incident follow-up, and technical planning.
- Worked across Python/FastAPI/Django services with PostgreSQL, Redis, Kafka, RabbitMQ, Airflow, Spark, and Kubernetes.
- Improved ORM-heavy workflows, introduced migrations and configuration-driven processing, and strengthened observability and testing practices.
Language: Python, SQL
Backend: FastAPI, Django, Django REST Framework, SQLAlchemy, Pydantic, asyncio, aiohttp, Celery, REST, gRPC
Data & messaging: PostgreSQL, Redis, Kafka, RabbitMQ, Airflow, PySpark, Hadoop
Infrastructure: Docker, Kubernetes, GitLab CI/CD, Jenkins
Quality & observability: Pytest, unit and integration testing, Prometheus, code review, performance optimization, system design
| Project | What it demonstrates |
|---|---|
| Dev Interview Notes | Full-stack learning platform with React, TypeScript, FastAPI, PostgreSQL, async SQLAlchemy, JWT authentication, RBAC, Alembic migrations, Docker, and OpenAPI |
| MENU-FastAPI | Async REST API with PostgreSQL, Redis caching, Celery + RabbitMQ background jobs, Excel report generation, Docker Compose, and Pytest |
| GhostDB | Educational in-memory key-value database with a SQL-like CLI, nested transactions, COMMIT, and ROLLBACK |
| Balancer | Traffic-routing prototype using FastAPI, configurable origin/CDN redirects, Docker Compose, and a Locust load-test script |
| E-commerce | Django e-commerce platform with users, seller stores, products, discounts, baskets, and orders |
- Prefer simple, explicit boundaries: router → service → repository.
- Treat observability, tests, migrations, and operational tooling as part of the feature.
- Optimize the real bottleneck and measure the result.
- Make distributed workflows understandable, debuggable, and safe to change.
- Communicate architecture clearly and help teams deliver consistently.
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