Вакансии

Обеспечивать работоспособность географически распределенной инфраструктуры и сервисов (у нас 1000+ серверов, 2 ДЦ по Москве + ЦОД в офисе). Работы с системой виртуализации (OpenNebula, Ceph): создание, удаление, миграция ВМ, устранение проблем в работе системы виртуализации, оптимизация ресурсов.

Управление распределённой инфраструктурой, обеспечение стабильности и отказоустойчивости систем. Работа с облачными платформами OVHcloud, AWS, Azure.

Location: London, United Kingdom Workplace: none About InvestEngine InvestEngine is a fast-growing, London-based fintech on a mission to make ETF investing simple and accessible. More than 150,000 customers trust us with over £3.5 billion in assets, and we’ve been featured by Forbes, MoneyWeek and Finder. And we’re just getting started. About the Role We’re looking for a Middle Backend Engineer to join our Backoffice team. Backoffice is the technical foundation behind our growing business: the internal systems, workflows and integrations that Operations, Compliance and Business teams rely on every day. As the company scales, there are more processes to automate and more systems to strengthen than the current team can deliver. This isn’t a role for fast ticket execution. You’ll own problems end-to-end: understand how a back office process actually works, find where it can be automated or made more reliable, design a sound solution, align with the teams who depend on it and drive it through to production. It’s a hands-on engineering role with no people management. What You’ll Do Build and maintain backend services in Python 3.11 with Django / DRF Automate back office processes and workflows, working directly with Product, Compliance and Business teams Question proposed solutions and help shape the right one before building it Design and improve REST APIs for internal and external integrations Work with MySQL and Redis to improve performance, reliability and scalability Investigate and resolve production issues, find bottlenecks and reduce technical debt Contribute to architecture discussions and technical decisions in your area Improve our development processes and internal tooling Keep code quality, testing and engineering practices at a high standard Work with Git and CI/CD pipelines (TeamCity, GitHub Actions) What We’re Looking For 3+ years of backend development experience Strong Python skills and good knowledge of Django or a similar framework Solid understanding of relational databases, caching, REST APIs and service design Experience troubleshooting production issues Ownership: you can take a task and see it through independently Familiarity with Git, CI/CD practices and modern development workflows Good communication and problem-solving skills English at B2 level or higher, and fluent Russian Nice to Have Experience in fintech, regulated industries or internal B2B tools AWS and Docker Message brokers such as RabbitMQ or Kafka Open-source contributions or side projects No prior fintech experience is required. How We Work We’re a lean, collaborative engineering team where developers have real influence on architecture, product direction and engineering culture. You’ll have autonomy, trust and support while working on business-critical systems that scale with the company, without micromanagement or unnecessary bureaucracy. What We Offer Impact from day one on the systems that keep the business running Growth opportunities and increasing responsibility over time Influence on architecture and the long-term development of our platform Modern stack: Python 3.11, Django, MySQL, Redis, AWS, GitHub, TeamCity Fully remote, work from anywhere Unlimited vacation A supportive and experienced engineering team Hiring Process HR interview with our Talent team Technical interview with the Backend team Gamified cognitive assessment to understand how you think and problem-solve Final conversation with our CTO

- 8+ years of experience in both Automation&Manual testing methodologies;

Оборудование HP/Nutanix/Fortinet/CISCO. Сопровождение и актуализация виртуальных сред ESXi/Nutanix, VDC/VPS.

Zada: Tizim administratori lavozimi uchun ishchi qidirilmoqda. Talablar: — Kamida 6 yil ish tajribasi talab qilinadi. — PHP, SQL, MySQL, PostgreSQL, Nginx, Apache, Linux, 1C, Bitrix, Windows Server texnologiyalari bilan ishlash tajribasi kerak. Shartlar: — Ish joyi Toshkent shahrida, ofisda (offline) ishlash. — Maosh 8 000 000 dan 12 000 000 so'mgacha. Qo'shimcha ma'lumotlar va ariza topshirish uchun havola: https://tashkent.hh.uz/vacancy/139105655

Развитие продукта и решение задач по расширению бизнес функционала; Поддержка текущего решения;

Location: Canada What The Role Is We're hiring a Staff MLE for the Discovery team to own recommendations and personalization across Babylist's consumer experience — the homepage feed, product recs, search, and the ML-powered systems that make registry building feel effortless. Babylist was built on editorial recommendations: products chosen by people with deep baby gear expertise. That editorial foundation is a big part of why millions of families trust us. We're now building ML-powered personalization on top of it, using one of the richest first-party datasets in parenting. We're early in this work, and we have a real mandate. A small Discovery team has initial retrieval and reranking models live on parts of the site and a steady cadence of A/B tests. We are looking for a Staff MLE who has seen personalization done well at scale and can set the technical direction for where we go next. Registry building is the heart of the Babylist product. Every parent builds a list of dozens of products, from swaddle to stroller, with real stakes (a friend or family member is going to buy these things, and a baby is going to use them). As a universal registry, this registry also typically spans many retailers. This makes registry building on Babylist one of the most interesting personalization problems in consumer e-commerce: latent intent, life-stage progression, multi-stakeholder gift dynamics, deep declarative signal in millions of completed registries, cross-retailer datapoints, and a user who genuinely wants help. If you want to join a mature ML org and tune models at the margins, this isn't the right role. If you've worked inside a strong recommendations team, learned what good looks like, and want to build from zero-to-one at a company earlier in the journey, read on. What You'll Own You'll be the technical lead for personalization on the Discovery team, working alongside a PM, Engineering Manager, Senior MLE and fullstack software engineers. You set where our models go over the next year or two, sequence the bets that get there, and stay deep in building. A few examples of the problems you’ll get to shape: Product recommendations across every surface. Product detail pages, add-next recommendations after someone adds to their registry, vibes/aesthetic representations of products and the shared models underneath them. Personalizing the Feed. Deciding which products, editorial content and social content each family sees, and in what order. Personalizing search. Bringing what we know about a family into search results, and helping decide how much search should share retrieval and ranking with recommendations. Making Addie (our AI registry assistant) better at recs. Working with the team behind Addie so its product suggestions draw on the same models and signals as the rest of the site. In practice, you will: Take a fuzzy business problem from first sketch to production model, and own whether it moved the metric. Make the modeling and architecture calls that span surfaces and are expensive to reverse. Design the evaluation, offline and online, that tells us a model is good before and after it ships. Know when a rule or a simple baseline is the right first step, and when it's time to pursue more advanced approaches. Partner with product, design and data to help shape what's worth building. Coach engineers on the team, and more broadly be an ML expert and resource to the wider engineering org. Who You Are You’ve shipped recommendation and/or personalization systems that reached real users at scale within a consumer product, and can point to the business impact of your work. You’ve done this within a team that did ML well — you know what good looks like, and are motivated to bring that to a company earlier in their ML journey. You bring: Expertise in recommender fundamentals: for example, candidate generation vs. ranking, offline vs. online evaluation, cold start solutions, and handling position and popularity bias. Fluency in the Python ML stack and comfort owning models through deployment and monitoring, including contributions to our application backend. An ability to take an ambiguous problem and start moving before anyone hands you the full picture. A builder's instinct for early-stage ML. You understand effort vs. effectiveness tradeoffs and can advocate for when a rule beats a model, or a model is “good enough” for early learnings. An outcome and user focused mindset. You measure yourself by business impact, and you can connect a model change to user experience & registry adds, conversion or revenue. AI-native daily practice. You're already using AI to move faster and improve your output, and you stay curious about what's coming next. Compensation We post real numbers. For a Canada-based Staff Engineer, the starting base salary range is 299,300 to 372,600 CAD plus a target annual bonus of 20 percent of base. That's total target cash of roughly 359,160 to 447,120 CAD. On top of that you get meaningful equity and a 401(k) match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope. How We Build AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome. The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers. The Stack Core app: Rails, Packwerk, React, TypeScript, Sidekiq Mobile: iOS (Swift), Android (Kotlin) Data, search & events: MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma Machine learning: deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow AI & dev tooling: Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim Infra & ops: AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly Key integrations: Shopify (payments), Iterable (CRM) Why Babylist An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done. Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide. How We Work Remote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis. How We Hire Three rounds, usually two to three weeks start to finish. Recruiter conversation (30 minutes). Trade context: what you want next, what we're building and straight answers on comp, team and remote. Technical screen (1 hour). One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles. Final round (4 hours). Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours. If your timeline is tight, tell us and we'll move faster. Benefits Company-paid medical and fully covered dental and vision A 401(k) match Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program Winter Wonder Week, a paid company-wide week off at the end of the year A remote-work stipend Mental-health and wellness support A Few Things To Know We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws. We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking. If you have a family member or close relationship with a Babylist employee, let your recruiter know. Official outreach only ever comes from an @babylist.com address

Location: United States What The Role Is We're hiring a Staff MLE for the Discovery team to own recommendations and personalization across Babylist's consumer experience — the homepage feed, product recs, search, and the ML-powered systems that make registry building feel effortless. Babylist was built on editorial recommendations: products chosen by people with deep baby gear expertise. That editorial foundation is a big part of why millions of families trust us. We're now building ML-powered personalization on top of it, using one of the richest first-party datasets in parenting. We're early in this work, and we have a real mandate. A small Discovery team has initial retrieval and reranking models live on parts of the site and a steady cadence of A/B tests. We are looking for a Staff MLE who has seen personalization done well at scale and can set the technical direction for where we go next. Registry building is the heart of the Babylist product. Every parent builds a list of dozens of products, from swaddle to stroller, with real stakes (a friend or family member is going to buy these things, and a baby is going to use them). As a universal registry, this registry also typically spans many retailers. This makes registry building on Babylist one of the most interesting personalization problems in consumer e-commerce: latent intent, life-stage progression, multi-stakeholder gift dynamics, deep declarative signal in millions of completed registries, cross-retailer datapoints, and a user who genuinely wants help. If you want to join a mature ML org and tune models at the margins, this isn't the right role. If you've worked inside a strong recommendations team, learned what good looks like, and want to build from zero-to-one at a company earlier in the journey, read on. What You'll Own You'll be the technical lead for personalization on the Discovery team, working alongside a PM, Engineering Manager, Senior MLE and fullstack software engineers. You set where our models go over the next year or two, sequence the bets that get there, and stay deep in building. A few examples of the problems you’ll get to shape: Product recommendations across every surface. Product detail pages, add-next recommendations after someone adds to their registry, vibes/aesthetic representations of products and the shared models underneath them. Personalizing the Feed. Deciding which products, editorial content and social content each family sees, and in what order. Personalizing search. Bringing what we know about a family into search results, and helping decide how much search should share retrieval and ranking with recommendations. Making Addie (our AI registry assistant) better at recs. Working with the team behind Addie so its product suggestions draw on the same models and signals as the rest of the site. In practice, you will: Take a fuzzy business problem from first sketch to production model, and own whether it moved the metric. Make the modeling and architecture calls that span surfaces and are expensive to reverse. Design the evaluation, offline and online, that tells us a model is good before and after it ships. Know when a rule or a simple baseline is the right first step, and when it's time to pursue more advanced approaches. Partner with product, design and data to help shape what's worth building. Coach engineers on the team, and more broadly be an ML expert and resource to the wider engineering org. Who You Are You’ve shipped recommendation and/or personalization systems that reached real users at scale within a consumer product, and can point to the business impact of your work. You’ve done this within a team that did ML well — you know what good looks like, and are motivated to bring that to a company earlier in their ML journey. You bring: Expertise in recommender fundamentals: for example, candidate generation vs. ranking, offline vs. online evaluation, cold start solutions, and handling position and popularity bias. Fluency in the Python ML stack and comfort owning models through deployment and monitoring, including contributions to our application backend. An ability to take an ambiguous problem and start moving before anyone hands you the full picture. A builder's instinct for early-stage ML. You understand effort vs. effectiveness tradeoffs and can advocate for when a rule beats a model, or a model is “good enough” for early learnings. An outcome and user focused mindset. You measure yourself by business impact, and you can connect a model change to user experience & registry adds, conversion or revenue. AI-native daily practice. You're already using AI to move faster and improve your output, and you stay curious about what's coming next. Compensation We post real numbers. For a US-based Staff Engineer, the starting base salary range is $233,500 to $290,700, plus a target annual bonus of 20 percent of base. That's total target cash of roughly $280,200 to $348,840. On top of that you get meaningful equity and a 401(k) match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope. How We Build AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome. The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers. The Stack Core app: Rails, Packwerk, React, TypeScript, Sidekiq Mobile: iOS (Swift), Android (Kotlin) Data, search & events: MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma Machine learning: deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow AI & dev tooling: Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim Infra & ops: AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly Key integrations: Shopify (payments), Iterable (CRM) Why Babylist An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done. Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide. How We Work Remote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis. How We Hire Three rounds, usually two to three weeks start to finish. Recruiter conversation (30 minutes). Trade context: what you want next, what we're building and straight answers on comp, team and remote. Technical screen (1 hour). One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles. Final round (4 hours). Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours. If your timeline is tight, tell us and we'll move faster. Benefits Company-paid medical and fully covered dental and vision A 401(k) match Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program Winter Wonder Week, a paid company-wide week off at the end of the year A remote-work stipend Mental-health and wellness support A Few Things To Know We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws. We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking. If you have a family member or close relationship with a Babylist employee, let your recruiter know. Official outreach only ever comes from an @babylist.com address

💎 Stark Games требуется Backend‑разработчик для работы над крупным казуальным игровым продуктом с насыщенным liveops: предстоит реализовывать и дорабатывать игровые механики, участвовать в code review и оптимизации производительности, актуализировать техническую документацию, применять AI‑инструменты для генерации кода и документации.

-6-8+ years building full-stack products, including time as the senior person on the work. -Strong relational database skills, Postgres or MySQL: schema design, indexing, query tuning, transactions, and what actually happens under load.

Location: Москва Workplace: гибкий Наша команда развивает экосистемные сервисы VK — решения, которые упрощают пользовательский опыт и создают удобную экосистему из существующих продуктов внутри и за пределами VK. Сейчас мы находимся в поиске системного администратора в направление надёжности сервисов. Задачи Обеспечивать высокую надёжность, доступность и производительность сервисов проекта, живущих в разных средах, от bare-metal до облаков и K8s Участие в планировании и резервировании ресурсов под сервисы, в проработке архитектуры новых сервисов и оптимизации текущих Участие в решении инцидентов, оперативное реагирование на проблемы, разбор полётов Взаимодействие с командой разработки для решения общих задач и вопросов Требования Понимание устройства Linux (ядро, память, диски, процессы, траблшутинг) Знание стека TCP/IP и прикладных протоколов Практический опыт работы с Docker и Kubernetes Навыки использования Puppet, Ansible или других средств IaC Опыт в настройке и обслуживании БД, таких как MySQL и MariaDB, PostgreSQL, ClickHouse, Redis, веб-сервисов Nginx, Apache, uWSGI Умение читать и писать скрипты на Bash, Python и др. Умение автоматизировать свою работу

Ищем SRE в команду, которая отвечает за доступность, обновления и восстановление систем хранения и обработки данных. — диагностика сбоев, производительности и репликации платформы данных;

Разработка модулей на Python.

Location: Алматы Задачи Развитие и поддержка лучших IT-продуктов Казахстана; Создание нового и оптимизация работы имеющегося функционала; Повышение надежности и качества системы на всех уровнях; Работа над нашими новыми проектами. Требования Отличное знание PHP; Умение работать с инфраструктурой: Linux, Docker, Nginx; Знание баз данных: MySQL, MongoDB, Redis; Умение работать с Git и системами сборки и деплоя (GitHub Actions или другие CI/CD); Способность оценивать сроки выполнения задач и соблюдать их, вовремя сигнализировать о проблемах; Написание и поддержка тестов; Навыки мониторинга, логирования, сбора и анализа метрик для предотвращения и решения проблем; Огромное желание расти и развиваться профессионально. Будет плюсом Знание других языков программирования и баз данных; Понимание микросервисной архитектуры и подходов к межсервисному взаимодействию; Опыт работы с Kubernetes; Знание языка Go или желание его выучить.

Workplace: hybrid Обязанности Сбор прототипов витрин данных; Разработка и оптимизация SQL-запросов для извлечения данных из различных источников; Анализ и интерпретация больших объемов данных для выявления тенденций, паттернов и инсайтов; Формирование и проверка гипотез; Документирование разработанных решений; Взаимодействие с командами разработки и продуктового менеджмента для определения требований к данным; Участие в проектах по улучшению качества данных и автоматизации аналитических процессов. Условия работы Официальное оформление с белой ЗП, полугодовые премии по результатам работы; Возможность работать удалённо или в гибридном формате; Удобный офис рядом с м. «Добрынинская» / удобный офис с бесплатной парковкой на м. «Волгоградский проспект» / МЦД «Калитники»; Гибкий график работы; Широкий пакет ДМС (включая выезд за рубеж и стоматологию), страхование жизни и здоровья; Возможность учиться и развиваться за счёт компании: внешние тренинги и семинары по профессиональным тематикам, участие в крупнейших конференциях страны, программы развития цифровых и управленческих навыков (Цифровая академия, Школа Тимлидов), онлайн и офлайн мастер-классы, корпоративный университет «X5 Полка», школа наставников и многое другое; Программы мотивации для спикеров и авторов: внутренняя школа Speak Up School, Write Up, ачивки и баллы за выступления, подготовка спикеров к крупнейшим конференциям страны, сообщество авторов на Хабре, возможность шерить опыт с комьюнити внутренних и внешних экспертов; Обмен опытом через cообщества: 12 технических комьюнити от java до devops; Яркая корпоративная жизнь с большим количеством мероприятий, конкурсов и возможностей для творческой реализации: регулярные внутренние митапы, демо-дни, открытые микрофоны, день IT-специалиста, программы волонтерства, корпоративное предпринимательство X5 Idea Challenge; Забота о благополучии сотрудников: 7 спортивных сообществ (бег, футбол, волейбол, баскетбол, хоккей, лыжи, триатлон), ежегодная Неделя здоровья; Скидки в экосистеме бизнесов Х5 («Пятёрочка», «Перекрёсток», «Много лосося», «Перекресток Впрок»); Программа привилегий Prime-zone (скидки на товары и услуги и специальные предложения от компаний-партнёров); Материальная помощь сотрудникам, попавшим в сложную жизненную ситуацию. Профессиональные навыки Релевантный опыт работы от 3 лет; Уверенное знание SQL и опыт работы с реляционными базами данных (PostgreSQL, MySQL, MS SQL и др.); Умение писать и оптимизировать сложные SQL-запросы (Оконные функции, Рекурсии, Функции и операторы для работы с массивами, Временные таблицы); Умение разбираться в чужом коде, проводить ревью; Знание и опыт работы с устройством хранилища данных ( Data Vault 2.0 ); Опыт работы со стеком Greenplum, Hive, Clickhouse, S3, Trino; Хорошие коммуникативные навыки и умение работать в команде; Опыт работы в BI инструментах (Qlik Sense, Superset); Базовые навыки по работе с данными на Python (pandas, numpy, sqlalchemy, psycopg2, requests). Будет дополнительным плюсом: Работа в Airflow, умения написание дагов и знание основных компонентов; Работа в Ataccama.

Workplace: hybrid Обязанности Сбор требований с бизнес-заказчиков и анализ источников данных Разработка, реализация и поддержка типовых потоковых интеграционных решений на стеке технологий, принятых в команде: Trino, Iceberg, S3, Spark, Apache Airflow, Kafka, Cosmos, Flink Оперативное реагирование на информацию о проблемах в зоне ответственности, выполнение типовых задач в установленный срок Поддержание в актуальном состоянии документации типовых интеграционных решений платформы больших данных. Предоставление отчетности о своей деятельности руководителю. Условия работы Официальное оформление с белой ЗП Полугодовые премии по результатам работы Возможность работать удалённо или в гибридном формате Удобный офис рядом с м. «Добрынинская» / удобный офис с бесплатной парковкой на м. «Волгоградский проспект» / МЦД «Калитники» Гибкий график работы Широкий пакет ДМС (включая выезд за рубеж и стоматологию), страхование жизни и здоровья Возможность учиться и развиваться за счёт компании: внешние тренинги и семинары по профессиональным тематикам, участие в крупнейших конференциях страны, программы развития цифровых и управленческих навыков (Цифровая академия, Школа Тимлидов), онлайн и офлайн мастер-классы, корпоративный университет «X5 Полка», школа наставников и многое другое Программы мотивации для спикеров и авторов: внутренняя школа Speak Up School, Write Up, ачивки и баллы за выступления, подготовка спикеров к крупнейшим конференциям страны, сообщество авторов на Хабре, возможность шерить опыт с комьюнити внутренних и внешних экспертов Обмен опытом через cообщества: 12 технических комьюнити от java до devops Яркая корпоративная жизнь с большим количеством мероприятий, конкурсов и возможностей для творческой реализации: регулярные внутренние митапы, демо-дни, открытые микрофоны, день IT-специалиста, программы волонтерства, корпоративное предпринимательство X5 Idea Challenge Забота о благополучии сотрудников: 7 спортивных сообществ (бег, футбол, волейбол, баскетбол, хоккей, лыжи, триатлон), ежегодная Неделя здоровья Скидки в экосистеме бизнесов Х5 («Пятёрочка», «Перекрёсток», «Много лосося», «Перекресток Впрок») Программа привилегий Prime-zone (скидки на товары и услуги и специальные предложения от компаний-партнёров) Материальная помощь сотрудникам, попавшим в сложную жизненную ситуацию. Профессиональные навыки Общее представление о стеке технологий Lakehouse Понимание различия работы между BigData/Lakehouse и с данными обычного размера Знание SQL (индексы, функции, оптимизация, профилирование производительности) Опыт работы с любой реляционной БД (Oracle, Postgres, MySQL, MsSQL, DB2 и т.п.) Умение работать с Git (знание команд git pull/commit/push) Опыт работы с DBT, Cosmos Представление о работе с Trino Понимание устройства Iceberg, форматов данных Понимание работы с minio или любого другого хранилища на основе S3 Опыт использования системами ведения проектов и документации Знания основ программирования (JAVA, Python).

Workplace: Удаленно Объединённая компания Wildberries и Russ — это международная технологическая компания, образованная в результате слияния двух лидеров рынка — IT-компании Wildberries и оператора наружной рекламы Russ. Обязанности Работать с обращениями - диагностика, решение, маршрутизация, проработка плана по автоматизации процессов; Оперативно принимать организационные решения для скорейшего устранения инцидентов; Работать с инцидентами - диагностика, эскалация, координирование, инцидент-менеджмент (любой инцидент фиксируется и прорабатывается с целью исключить повторение проблемы); Работать с системами мониторинга; Формировать базу знаний и инструкции для поддержки; Участвовать в приеме новых проектов \ процессов \ сервисов в поддержку; Автоматизировать все рутинные \ повторяющиеся задачи; Формировать выборки и отчеты с помощью SQL-запросов; Ставить задачи на команды разработки c дальнейшей приоритизацией; Применять Postman\Swagger. Требования ​​​Имеете опыт работы в поддержке L2 от года (плюсом будет опыт работы в логистике e-com); Имеете опыт продуктовой поддержки; Понимаете принципы работы backend и frontend веб-приложений; Понимаете принципы работы SOAP веб-сервисов и REST API; Имеете базовое знание SQL, опыт работы с СУБД PostgreSQL, MySQL, MS SQL, или Oracle SQL; Умеете работать с логами в Kubernetes. Условия Обучение и развитие: языковые клубы, собственный корпоративный университет, программы развития управленческих навыков и многое другое; Благополучие сотрудников: корпоративный пакет ДМС со стоматологией, корпоративный спорт, консультации психолога и дополнительные возможности аккредитованной IT-компании; Множество сообществ: клуб спикеров, футбола, йоги, шахмат и т.д.; Забота о семьях: создаем условия, в которых легко сочетать карьеру и заботу о близких – от гибкого подхода до масштабных проектов для детей сотрудников; Скидки и партнерские программы: на обучение, страхование, покупки и многое другое; Комфортная рабочая среда: бесплатное питание в офисе, современные офисы рядом с метро, корпоративная техника и портал для сотрудников.