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Salary
$24k – $44k per year (Estimated)
Location
In office (Moscow)
Seniority
Middle
Overview
Company
Impact
Profile match
Ozon is a Russian e-commerce company founded in 1998 by the software house Reksoft as an online bookshop, and often described as the Amazon of its market for taking the same first-party plus marketplace approach. It now sells across every consumer category through a network of fulfilment centres, sorting hubs and tens of thousands of pickup points that reach far beyond the largest cities. The group has added an express grocery service, a travel booking arm and Ozon Bank, whose cards and lending are tied to the marketplace, and it is one of the two dominant online retail platforms in the country alongside Wildberries.

Ozon Банк - это современные финансовые технологии для людей и бизнеса. Мы сами реализуем ключевые системы: процессинг, учётное ядро, переводы. Даём возможность влиять на результат, ценим открытость и поддерживаем коллег.

Ищем дата-инженера в команду машинного обучения. Вам предстоит строить и поддерживать пайплайны больших данных для скоринга, антифрода и других ML-продуктов: от загрузки и обработки данных до доставки фичей и результатов моделей в продакшн.

Вас ждут задачи с большими объёмами данных, прямое влияние на запуск ML-продуктов и работа вместе с дата-сайентистами, MLOps-инженерами и аналитиками.

Вам предстоит:

  • разрабатывать batch-пайплайны на Spark/PySpark под управлением Airflow

  • готовить витрины и датасеты для обучения, скоринга и мониторинга моделей

  • работать с HDFS: организовывать слои и партиционирование данных, контролировать хранение и квоты

  • оптимизировать производительность и ресурсоёмкость Spark-задач

  • проектировать загрузку и хранение данных в ClickHouse, оптимизировать запросы и структуру таблиц

  • обеспечивать надёжность пайплайнов: DQ-проверки, мониторинг, алерты и разбор инцидентов

Для нас важны:

  • опыт разработки промышленных пайплайнов данных

  • уверенное знание Spark/PySpark, HDFS, Python и SQL

  • опыт работы с Airflow и ClickHouse

  • понимание распределённых вычислений, партиционирования и оптимизации ETL-процессов

Будет плюсом:

  • опыт работы с Kafka, Feature Store и данными для ML, скоринга или антифрода

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