Mikhail Perevalov
Python Developer
5+ years exp
20+ projects
Python
SQL
Active 20 days ago
+7 (983) 3118725 Invite to interview
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Overview
Technical skills
Timeline
Roles
Overview
A backend-focused generalist at a solid middle level (3.0/10) who writes production-oriented Django services. The strongest proven skill is building an asynchronous, provider-based notification system with concrete artifacts in notifications/services/notification_service.py and providers (including EmailProvider) showing timeouts, DB status transitions and structured error handling. There is little or no evidence of CI/CD pipelines, infrastructure-as-code with remote state, Kubernetes deployments, SLO/alerting or formal incident/runbook artifacts.
Technical skills
Python• Middle • 5y+ • 20+ projects
SQL• Middle • 3y+ • 20+ projects
Databases
MySQL
PostgreSQL• 3y+
RabbitMQ
Redis
AI/ML
Airflow
DevOps
Docker
Git
GitLab CI
Nginx
Rest API
Docker Compose
Kubernetes
CI/CD
Mobile
Clean Architecture
Timeline
Engineer
•
Middle
TN-GROUP
•
Full-Time
Developed and maintained backend services for an application related to satellite activity monitoring. Worked on bug fixes and participated in architecture decisions, including selecting a stack and implementation options for microservices. Implemented and supported CI/CD processes within the company.
CI/CD
Lead Python Developer
•
Lead
Sber Analytics
•
Full-Time
Built ETL pipelines by creating Airflow DAGs to load raw data from multiple sources including S3, Redis, and RabbitMQ. Performed transformations and stored results in a database. Supported deployments on internal and customer environments using Kubernetes and created dashboards for BI data marts.
Python
Airflow
Redis
RabbitMQ
Kubernetes
Automation Department Developer
•
Middle
Intelsib
•
Full-Time
Wrote Python scripts to automate internal company processes, including parsing and scraping websites. Monitored website availability for client systems, added new features to internal tools, and fixed bugs. Worked with PostgreSQL using SQLAlchemy for database operations.
Python
SQLAlchemy
PostgreSQL
Python Developer
•
Middle
Upwork
•
Freelance
Developed software and scripts and built web applications using Django and FastAPI. Implemented and improved website scraping functionality and worked on related backend features. Interfaced with databases as part of delivering and maintaining the implemented solutions.
Pythonsince 2021
Django
FastAPI
Novosibirsk State Technical University
Bachelor's Degree •
Microelectronics and Solid-State Electronics
Middle Backend Developer
Confidence: Medium API Engineer
A backend API engineer at a solid middle level who implements pragmatic, production-minded features in Django and DRF. The strongest proven skill is building a provider-based notifications flow with async concurrency and per-provider timeouts, as seen in the NotificationService and provider classes. The public code does not show advanced operational concerns - no queue-backed processing, limited transactional controls, no structured observability, and no test-suite evidence.
API Design
3/10
How well APIs are designed
Pragmatic DRF endpoints and input validation are present, but there is no explicit versioning, idempotency key handling, or documented error-contract strategy.
Evidence
mixei87/photopoint_testovoe/notifications/views.py: NotificationViewSet.send action (DRF action + request validation)
mixei87/photopoint_testovoe/notifications/views.py: HealthCheckView (simple health endpoint)
mixei87/photopoint_testovoe/users/serializers.py: request-aware email validation and password validation
Data Layer & Database
2/10
Working with databases
Clear ORM usage and an initial migration exist, but there is no evidence of explicit transaction boundaries, migration chain evolution, or tuned queries/indexes.
Scalability & Performance
3/10
Handling load and speed
Concurrency and timeouts are used (asyncio, background threads, per-provider timeouts), but there is no queue-based decoupling, caching strategy, connection tuning, or load-test evidence.
System Architecture
3/10
Overall system structure
Reasonable module separation (service + provider abstractions) and clear responsibilities, but the system is monolithic with no multi-service decomposition or config/secret management artifacts.
Evidence
Security & Auth
3/10
Protecting data and access
Basic auth and input validation practices are present (DRF permissions, password validators), but token lifecycle, revocation, explicit input sanitization, and dependency/secret hygiene are not evident.
Evidence
mixei87/photopoint_testovoe/notifications/views.py: NotificationViewSet.permission_classes = [IsAuthenticated]
mixei87/photopoint_testovoe/users/serializers.py: ChangePasswordSerializer uses Django validate_password
mixei87/photopoint_testovoe/notifications/services/providers/email_provider.py: reads EMAIL_API_KEY and EMAIL_SENDER_EMAIL from settings
Reliability & Observability
3/10
Stability and monitoring
Good use of logging, exception handling, and per-call timeouts; lacks structured tracing/correlation IDs, metrics, exponential backoff retries, and graceful shutdown patterns.
Evidence
mixei87/photopoint_testovoe/notifications/services/notification_service.py: exception handling and mark_as_failed paths with logging and asyncio timeouts
mixei87/photopoint_testovoe/notifications/views.py: logging around background batch runs and error logging
mixei87/photopoint_testovoe/notifications/services/providers/email_provider.py: logs non-2xx responses and exceptions
Expertise
Messaging & Real-time• Middle
Python• Middle
Microservices & API Architecture• Middle
Technologies
PostgreSQL• 3y+
Recommendations
- Lead development of notification and provider-integration features (new channels, provider fallbacks, provider health checks).
- Migrate background sending to a queue-backed worker (Celery or similar), add idempotency and retry/backoff, and document transaction boundaries for DB updates.
- Add structured logging/tracing and metrics (correlation IDs, Prometheus/OTel) and unit/integration tests for provider adapters and failure scenarios.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Middle DevOps Engineer
Confidence: Medium Generalist
A backend-focused generalist at a solid middle level (3.0/10) who writes production-oriented Django services. The strongest proven skill is building an asynchronous, provider-based notification system with concrete artifacts in notifications/services/notification_service.py and providers (including EmailProvider) showing timeouts, DB status transitions and structured error handling. There is little or no evidence of CI/CD pipelines, infrastructure-as-code with remote state, Kubernetes deployments, SLO/alerting or formal incident/runbook artifacts.
CI/CD Pipelines
1/10
Automated build and deploy
Almost no CI/CD engineering present - no reusable workflows, signing, concurrency groups or pipeline configs. Only incidental repo-level build artifacts exist.
Infrastructure as Code
1/10
Managing servers with code
Minimal IaC: only container definitions and compose orchestration; no Terraform/Pulumi modules, remote state or infra tests.
Containerization & Orchestration
3/10
Working with containers
Basic containerization with a Dockerfile and a reasonably configured docker-compose (healthchecks, restart policy, depends_on). No Kubernetes manifests or advanced orchestration patterns.
Observability & Monitoring
2/10
Watching system health
Light observability: application exposes a healthcheck endpoint and code uses structured logging, but there are no SLOs, alert rules, dashboards as code or alert routing.
Reliability & Incident Response
3/10
Keeping systems up
Demonstrates some reliability practices - timeouts, non-blocking background work, DB status transitions and defensive error handling - but no incident runbooks, chaos experiments or deployment rollback strategies.
Evidence
mixei87/photopoint_testovoe/notifications/services/notification_service.py: asyncio.wait_for timeouts and notification state transitions
mixei87/photopoint_testovoe/notifications/views.py: background thread launching asyncio.run with a bounded timeout
mixei87/photopoint_testovoe/notifications/services/providers/email_provider.py: HTTP request timeout and error handling
Cloud & Cost Optimization
1/10
Smart use of the cloud
No cloud-specific cost optimization or autoscaling evidence; only container images and compose-based local deployment are present.
Technologies
Docker Compose
Recommendations
- Own development of backend notification and async services - extend the existing provider pattern and add integration tests and provider mocks.
- Add CI/CD pipelines (reusable workflows, artifact management, and deploy gates) and canonicalize Docker image builds for reproducible releases.
- Implement basic observability and SLOs - export metrics, add alerting rules and ship dashboards as code tied to the HealthCheck endpoint.
- Migrate compose deployment to a simple stage-aware IaC (Helm/K8s or Terraform for cloud infra) and add rollout/rollback strategy documentation and runbooks.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: