Инженер данных

Magnit • Russian: C1 • Commerce • Logistics • Python • ETL/ELT • 6 hours ago
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Salary
≈ $16k – $33k per year (Estimated)
Location
In office (Moscow)
Seniority
Junior · 2+ years exp

Confirmed on the employer's own hiring board on Sep 25, 2026. First seen by Alion on Sep 25, 2026. Magnit scores A on the Alion truth index.

Overview
Company
Impact
Profile match

Magnit

Magnit is one of Russia's largest food retailers, running convenience stores, supermarkets, hypermarkets, cosmetics shops and pharmacies alongside online delivery and click-and-collect. Founded in 1994 in Krasnodar by Sergey Galitsky, it now operates tens of thousands of stores across the country, supported by its own logistics centres, transport fleet and food production facilities. The company is headquartered in Krasnodar with a large Moscow office, and hires store, warehouse and driving staff as well as corporate specialists in analytics, law, marketing and human resources.
magnit.ru • HQ: Krasnodar, Russia • Logistics • Online Groceries • Commerce • 1001-5000 employees • Est. 1994

Чем ты будешь заниматься:

  • Проектировать и разработать ETL-процессы (batch).
  • Создавать backend-сервисы на Python для обработки данных и интеграции с внешними системами.
  • Инжиниренно оптимизировать ML-проекты: рефакторить и оборачивать в продакшен пайплайны обучения и инференса, обеспечивая стабильность, воспроизводимость и наблюдаемость.
  • Участвовать в проектировании хранилищ данных и оптимизации существующих решений.
  • Стек технологий: Язык и фреймворки: Python 3, PySpark, Pandas, FastAPI, Pydantic Хранилища и вычисления: PostgreSQL, ClickHouse, Greenplum, Trino, S3 Оркестрация и ML: Apache Airflow, MLflow Инфраструктура: Docker, Kubernetes, Git, GitLab CI/C

Мы ожидаем

  • Опыт коммерческой разработки на Python не менее 2 лет; владение стандартной библиотекой и экосистемой data-разработки.
  • Понимание принципов построения ETL/ELT-процессов.
  • Опыт работы с распределёнными СУБД (Greenplum, ClickHouse) и вычислительными движками (Apache Spark, Trino).
  • Оркестрация пайплайнов в Airflow.
  • Верхнеуровневое понимание жизненного цикла ML-моделей.
  • Основы CI/CD и DevOps: сборка, тестирование, деплой.
  • Навыки работы в командной строке Linux.
  • Контейнеризация (Docker).
  • Использование ИИ-ассистентов в разработке (opencode или аналоги) для ускорения написания и ревью кода.
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