Вакансии

Задачи: — Подготовка данных: выгрузки из PostgreSQL и POS, очистка, пропуски, выбросы — Прогнозные модели: поток гостей, спрос на позиции меню, потребность в закупках — Признаки предметной области: сезонность, праздники, погода, история продаж — Валидация: разбиение по времени, контроль утечек, бизнес-метрика рядом с технической — Продуктовые исследования: сегменты гостей, средний чек, отток, эффективность акций — Передача модели в прод вместе с бэкендом: контракт данных, инференс, мониторинг качества — Код-ревью, обсуждение гипотез, планирование задач Требования: — Python 3.11+, аннотации типов — pandas, NumPy: группировки, join'ы, оконные функции, даты — scikit-learn: пайплайны, кросс-валидация, подбор гиперпараметров — Метрики и переобучение: MAE / MAPE, бейзлайн, утечки — SQL: join'ы, агрегаты, оконные функции — EDA: matplotlib или seaborn — Git, ветки, PR, код-ревью — Плюсом: statsmodels, Prophet, CatBoost, Docker, FastAPI, Airflow, MLflow, LLM и RAG Условия: — Удалённо из России, свободный график — Доступы к AI-сервисам (ChatGPT, Claude и другие) — Обучение за счёт компании: курсы, книги, конференции, хакатоны — Ревью каждые 6 месяцев, путь до Middle+ за 0.5–1 года

Обязательно: - Опыт оценки работ и распределения загрузки по команде. - Знание критериев качества данных: полнота, согласованность, актуальность. - Опыт формализации правил качества данных в виде проверок и технической документации. - Хорошее владение SQL. - Практический опыт работы с Oracle и PostgreSQL. - Опыт разработки ETL-процессов. - Опыт разработки витрин данных. - Базовые навыки Python и Pandas. - Базовые навыки работы с Apache Airflow. - Опыт проведения code review и согласования MR. - Опыт сопровождения и мониторинга информационных систем, инфраструктуры и интеграционных взаимодействий. - Опыт настройки или модернизации мониторинга. - Опыт анализа инцидентов систем обработки и контроля качества данных. - Опыт участия в релизном процессе и установке релизов. - Опыт работы с ролевыми моделями и требованиями ИБ. - Опыт участия в DR-тестировании. - Опыт ведения сервисной, функциональной и технической документации. Необходим опыт: - Опыт работы с Informatica ETL. - Опыт работы с FineBI. - Опыт сопровождения систем контроля качества данных. - Опыт работы с системами регистрации и маршрутизации инцидентов. Задачи: - Мониторинг доступности и работоспособности ИС, её компонентов, инфраструктуры и интеграционных взаимодействий. - Настройка и модернизация мониторинга и алертинга. - Анализ инцидентов, логов и результатов автоматических проверок качества данных. - Разработка и сопровождение ETL-процессов и витрин данных. - Проведение code review, согласование MR и контроль соблюдения архитектурных требований. - Декомпозиция задач, оценка работ и распределение загрузки внутри команды. - Приёмка функциональной, технической и сервисной документации. - Ведение admin guide, паспорта ИС, базы знаний и эксплуатационных регламентов. - Сопровождение ролевой модели и выполнение требований ИБ. - Приёмка изменений, установка релизов и проведение пост-релизных проверок. - Участие в DR-тестировании. - Координация устранения критических инцидетов

Задачи: — Перевод исследовательских DS рабочих процессов (например, прокси-метрики, модели устаревания) в производственные ML пайплайны с использованием Airflow, WandB и Ray, создание повторно используемых шаблонов для других команд. — Применение и внедрение методов причинно-следственного вывода с использованием производственного ML стека (propensity scoring, IPW, TMLE) для решения важных задач измерения за пределами экспериментальных возможностей. Создание инструментов самообслуживания для неэкспертов для получения строгих причинных выводов в масштабе. — Сотрудничество с ML инженерами и продуктовыми командами для выявления возможностей улучшения инструментов, метрик и методов измерения, что приводит к значительному улучшению качества моделей и бизнес-результатов. — Использование богатых метаданных и сигналов вовлеченности Pinterest для построения дата-дривен фреймворков, от важности признаков до деиндексации контента, для повышения эффективности и скорости платформы. — Проектирование и создание централизованных инструментов ML платформы для улучшения создания, оценки и доверия к признакам и моделям, включая производственные системы, работающие ежедневно в масштабе для всех моделей. Требования: — Опыт работы не менее 2 лет в роли прикладного ученого данных, ML инженера, исследователя или разработчика ПО с значительным опытом внедрения ML в производство. — Сильные навыки Python; опыт работы с PyTorch или аналогичными фреймворками глубокого обучения; знакомство с распределенными вычислениями (Spark, Ray), предпочтительно Ray. — Энтузиазм к созданию инструментов и платформ, которые увеличивают влияние всей ML организации, а не решают разовые задачи. — Глубокие знания теории ML с сильными фундаментальными основами для анализа моделей ML с первых принципов. — Владение лучшими практиками разработки ПО, включая контроль версий, код-ревью и воспроизводимые ML пайплайны. — Опыт работы с инструментами управления рабочими процессами (Airflow, Prefect, Jenkins или аналогичными) для надежной оркестрации ML пайплайнов. — Степень бакалавра или магистра в релевантной области, например, компьютерных наук, или эквивалентный опыт. Условия: — Работа удаленная с необходимостью присутствия в офисе 3-5 раз в квартал для личного взаимодействия. — Позиция не предусматривает помощь с релокацией. Дополнительно: — Компания предоставляет равные возможности для всех кандидатов без дискриминации. — Зарплатный диапазон от $114,297 до $235,319 в год, также возможна доля в компании. — Работа в США, предпочтительно в Пало-Альто, Калифорния, или удаленно по США.

Digital подразделение крупной производственной компании в поисках Data Engineer (Middle / Senior) в команду, которая строит Корпоративное Хранилище Данных и data-платформы для всего бизнеса. Основной стек проекта: Язык программирования Python Базы данных Greenplum и PostgreSQL Фреймворк Data Build Tool (DBT) Фреймворк Spark Брокер сообщений Kafka Оркестратор Airflow Инфраструктура: Kubernetes Управление инфраструктурой: Terraform, Ansible Управление кодом: GitLab, SonarQube Мониторинг: Prometheus, Grafana, AlertManager Задачи: — Разработка и поддержка ETL/ELT-процессов в Корпоративное Хранилище Данных — Организация и оптимизация пайплайнов данных — Разработка витрин данных и обеспечение качества данных — Настройка и поддержка инфраструктуры данных (Airflow, Spark, Kafka, DBT) — Взаимодействие с заказчиками: от сбора требований до внедрения и сопровождения решений — Документирование и презентация результатов Требования: — Опыт работы Data Engineer от 5 лет — Высшее образование (IT/математика/математическая статистика/физика) — Отличное знание Python и SQL — Опыт работы с реляционными БД (Greenplum / PostgreSQL) — Знание современных технологий обработки больших данных — Опыт с Airflow, Spark, Kafka, DBT — будет преимуществом — Умение работать в команде, сильные аналитические навыки и ответственность Условия: — Официальное оформление по ТК РФ — Возможность выбора формата работы (удаленка, гибрид, офис) — Развитый социальный пакет, ДМС, бонусы

МТС Банк – одно из ключевых направлений экосистемы МТС. Используются современные цифровые решения и развиваются технологичные банковские сервисы, доступные каждому. Команда занимается финансовой и управленческой отчетностью. Основная задача — работа с хранилищем данных (DWH), построение финансового результата, расчётов и отчётов. Ключевая предметная область: финансовый учёт в банке (бухгалтерские счета, проводки, локация). Текущий стек — Oracle, в планах переход на современные инструменты (Spark, Airflow). Задачи: — взаимодействовать с бизнес-заказчиками по вопросам сбора, анализа и формализации требований к данным для обязательной отчетности — выбирать системные решения для обеспечения полноты, доступности и качества необходимых данных — документировать, систематизировать и унифицировать витрины данных и аналитической отчетности — строить логические модели данных, описания показателей отчетности — взаимодействовать с командой разработки хранилища данных и аналитическое сопровождение процесса разработки — принимать участие в тестировании и приёмке готового функционала Требования: — понимание архитектуры и принципов работы хранилищ данных и аналитических систем — понимание основных задач и принципов управления данными — опыт разработки или бизнес-анализа витрин, отчетности на базе хранилища данных — опыт разработки документов (FSD, модель данных, методики расчета показателей) — опыт построения эффективных коммуникаций, проведения совещаний, выявления и формулирования бизнес и технических требований — знание SQL на уровне написания базовых запросов (предпочтительно СУБД Oracle) — знание Excel на продвинутом уровне — опыт работы с BI средствами на уровне аналитика/разработчика (предпочтительно MS Power BI)

Компания МТС Банк ищет системного аналитика DWH (Oracle). Команда занимается финансовой и управленческой отчетностью, основная задача — работа с хранилищем данных (DWH), построение финансового результата, расчётов и отчётов. Ключевая предметная область: финансовый учёт в банке (бухгалтерские счета, проводки, локация). Текущий стек — Oracle, в планах переход на современные инструменты (Spark, Airflow). Задачи: — взаимодействовать с бизнес-заказчиками по вопросам сбора, анализа и формализации требований к данным для обязательной отчетности — выбирать системные решения для обеспечения полноты, доступности и качества необходимых данных — документировать, систематизировать и унифицировать витрины данных и аналитической отчетности — строить логические модели данных, описания показателей отчетности — взаимодействовать с командой разработки хранилища данных и аналитическое сопровождение процесса разработки — принимать участие в тестировании и приёмке готового функционала Требования: — понимание архитектуры и принципов работы хранилищ данных и аналитических систем — понимание основных задач и принципов управления данными — опыт разработки или бизнес-анализа витрин, отчетности на базе хранилища данных — опыт разработки документов (FSD, модель данных, методики расчета показателей) — опыт построения эффективных коммуникаций, проведения совещаний, выявления и формулирования бизнес и технических требований — знание SQL на уровне написания базовых запросов (предпочтительно СУБД Oracle) — знание Excel на продвинутом уровне — опыт работы с BI средствами на уровне аналитика/разработчика (предпочтительно MS Power BI)

Задачи: — разрабатывать и поддерживать DAG в Apache Airflow — работать со скорингом (рассчитывать стратегии взыскания клиентов) — программировать на Python (основном языке) — оптимизировать запросы и работать с Oracle (SQL, хранимыми процедурами) — поддерживать и сопровождать выгруженные DAG — участвовать в разработке и применении моделей (машинном обучении, сегментации клиентов) — проводить код-ревью — самостоятельно проводить аналитику (в команде нет системных аналитиков) Требования: — уверенное коммерческое программирование на Python — опыт создания DAG и работы с Apache Airflow — опыт оптимизации запросов в Oracle, знание хранимых процедур — базовые навыки Linux (сборка образа, решение задач без DevOps) — опыт работы с Kubernetes — уровень Middle: около 2–3 лет коммерческой разработки на Python — релевантный опыт с Airflow 2–3 года — опыт именно оптимизации запросов в Oracle, а не просто написания простых запросов

Компания МТС Банк ищет специалиста на позицию ML/DevOps Engineer в Москве. Задачи: — деплоить и интегрировать модели, сопутствующие сервисы и инструменты, автоматизировать поставку и оптимизировать потребление ресурсов — мониторить и поддерживать работоспособность — обеспечивать соответствие политикам информационной безопасности и стандартам надёжности при развёртывании ML‑решений Требования: — уверенное администрирование Linux, понимание сетевых протоколов и потенциальных точек отказа — опыт конфигурации и распределения мощностей видеокарт для обучения и инференса моделей — опыт настройки мониторинга инфраструктуры и метрик деградации моделей — опыт работы с vllm, Triton Inference Server, FastAPI, KServe или TorchServe — опыт построения сложных многоэтапных пайплайнов с упором на безопасность Технологический стек включает Docker, Linux, Kubernetes, Bash, GitLab CI, Ansible, Terraform, Airflow, Python, vllm, cuda, triton, Prometheus/Victoria Metrics, Grafana, SQL, Postgres, Elasticsearch, MLflow, Qdrant, Nexus, Vault.

Задачи: — Разрабатывать новые отчеты и поддерживать существующие; — Автоматизировать подготовку и доставку отчетов с помощью Python; — Проектировать концепты дата-моделей и витрин для своих отчетов; — Участвовать в проверке данных для витрин в продакшене; — Плотно взаимодействовать с разными командами: CRM, антифрод, маркетинг, финансы, випы и т.д.; — Поддерживать актуальность и прозрачность показателей, документировать метрики и логику расчетов; — Участвовать в разработке различных бизнес-моделей; — Создавать и дорабатывать дашборды в Tableau; — Искать возможности для автоматизации и улучшения существующих решений. Требования: — Уверенное владение основными конструкциями Python и понимание принципов работы языка; — Практический опыт использования функций, генераторов, декораторов и т.д.; понимание принципов организации кода: модули, пакеты, зависимости; — Умение писать надежный код с обработкой исключений и логированием; — Опыт работы с Pandas; — Опыт работы с Git; — SQL: умение читать большие сложные витрины, дебажить их и грамотно писать запросы; — Понимание принципов оптимизации запросов и работы с большими объемами данных; — Tableau или любой другой BI тул: базовый уровень. Будет плюсом: — Опыт работы с Airflow. Условия: — Удалённая работа из любой точки мира с возможностью посещения офиса в Москве; — Достойный уровень заработной платы; — Сильная техническая команда и обмен опытом; — Сложные задачи и использование новых технологий; — Отсутствие бюрократии и технологическая свобода; — Возможность карьерного роста и профессионального развития; — Открытая и свободная атмосфера; — 50% компенсация оплаты изучения английского языка; — Частичная оплата занятий спортом.

Задачи: — Строить и развивать CI/CD-пайплайны в GitLab (GitLab CI/CD): от структуры и раннеров до стратегий деплоя (blue-green, canary, rollback); — Писать и поддерживать Ansible-плейбуки для конфигурации серверов, раскатки приложений и рутинных операционных задач; — Разрабатывать автоматизацию для DBA-команды: скрипты обслуживания БД (Clickhouse, Redis, PostgreSQL), автоматизация бэкапов, ротации, мониторинга, регламентных проверок (Python/Bash); — Писать скрипты и небольшие инструменты по запросу инженерных команд - на Python и Bash, с адекватным логированием и обработкой ошибок; — Поддерживать и улучшать инфраструктуру как код (Docker, в перспективе - Helm-чарты и Kubernetes-манифесты); — Участвовать в код-ревью инфраструктурных изменений и в разборе инцидентов, связанных с CI/CD и автоматизацией; — По мере роста задач - подключаться к работе с Kubernetes, Kafka, RabbitMQ, Airflow, Nginx и тд (администрирование, автоматизация обслуживания, участие в проектировании). Требования: — Опыт построения и поддержки CI/CD от 2 лет, желательно с GitLab CI/CD; — Уверенное написание Ansible-плейбуков (роли, инвентари, идемпотентность); — Python на уровне написания production-скриптов; — Bash на уровне уверенной автоматизации операционных задач; — Опыт работы с Docker; — Понимание принципов работы Linux на уровне администрирования (сеть, права, systemd, файловые системы); — Готовность разбираться в предметной области смежных команд (специфика задач DBA) для написания релевантной автоматизации. Будет плюсом: — Опыт работы с Kubernetes (деплой, отладка, чтение манифестов) и/или Helm; — Опыт работы с Kafka, RabbitMQ, Airflow, DataHub, Nginx — администрирование или автоматизация обслуживания; — Опыт автоматизации задач вокруг Clickhouse, Redis, PostgreSQL; — Опыт настройки мониторинга (Prometheus/Grafana) и алертинга (Alertmanager). Условия: — Официальное оформление в IT-аккредитованную компанию и зарплата на уровне топовых IT-компаний России; — Удалённый формат работы, гибкое начало рабочего дня + 3 day off в год; — Расширенный ДМС со стоматологией, экстренной и плановой госпитализацией и др.; — Выдача необходимой техники для работы (Mac/Windows на выбор); — Возможность посещать профессиональные конференции и выступать от имени компании на отраслевых мероприятиях; — Performance Review дважды в год с возможностью влиять на профессиональное развитие и уровень дохода; — Сотрудничество с BestBenefits - сервис корпоративных скидок, занятия с преподавателем-носителем по английскому языку; — Возможность получить сниженную процентную ставку на ипотеку; — Корпоративная скидка 50% на сервис каршеринга.

Задачи: — участие в создании и поддержке аналитических решений для повышения эффективности маркетинговых кампаний; — применение методов машинного обучения, особенно uplift-моделирования, для оптимизации взаимодействия с клиентами и повышения ROI; — построение uplift-моделей по сегментам для разных групп; — разработка uplift-моделей для различных номиналов промоакций с последующей оптимизацией; — прогнозирование RFM; — участие в проведении A/B тестов, расчет финансовых эффектов от внедрения моделей, генерация признаков для моделей. Требования: — базовое понимание статистики и теории вероятностей; — опыт работы с Python (Pandas, NumPy); — знание основ машинного обучения (линейная регрессия, деревья решений, случайный лес); — понимание концепции uplift-моделирования; — опыт работы с табличными данными; — понимание принципов распределенных вычислений (PySpark); — опыт коммерческой разработки. Технологии и инструменты: — работа с данными: pyspark, SQL, S3; — классический ML и uplift: Scikit-learn, XGBoost, LightGBM, CatBoost, scikit-uplift; — разработка и версионирование: Jupyter Notebook, VS Code/PyCharm, git; — ML инфраструктура: JupyterHub, MLFlow, Airflow, PostgreSQL; — ML: Transformers, SGLang, Triton; — процессы: Jira, Confluence.

О позиции Разработка дашбордов и аналитики на Apache Superset с элементами AI для поддержки экспертизы лекарственных средств и медицинских изделий. Обязанности • Разработка и поддержка интерактивных дашбордов в Apache Superset • Написание и оптимизация сложных SQL-запросов, создание витрин данных • Кастомизация дашбордов (Jinja, CSS, Python) • Внедрение AI/ML-решений для аналитики и прогнозирования • Взаимодействие с заказчиками, сбор требований, презентация результатов Требования • Высшее техническое / ИТ-образование • Опыт BI-разработчика от 2 лет • Опыт работы с Apache Superset от 1 года • Продвинутый SQL • Опыт работы с PostgreSQL / ClickHouse / MS SQL • Знание Python (Pandas, SQLAlchemy) • Понимание DWH и моделирования данных Будет плюсом: опыт AI/ML в аналитике, Power BI/Tableau, Airflow, работа в фарме или госсекторе. Условия • Зарплата от 500 000 тенге • Работа в офисе в Астане • Социальный пакет по ТК РК • Участие в проектах цифровизации государственной экспертизы

Location: New York City, New York What you’ll be part of Garner is on a mission to transform the U.S. healthcare system — and we’re the only proven player doing exactly that. We partner with employers to redesign how healthcare works: applying 550+ proprietary clinical metrics across 80+ specialties to a dataset of 320M+ patients to identify the best-performing doctors, then using compelling incentives to steer members to the care that helps them get healthier, faster. The result is a rare “win win” — better care and lower costs for both members and employers. In just five years, our work has helped over 2.5 million people access higher-quality care and saved $1B in healthcare costs. We recently raised our Series E and have doubled five years running. If you've ever wanted your work to solve a problem that touches every person in this country, this is the opportunity to do exactly that. You'd be joining a team fundamentally reimagining healthcare in the U.S. — and using AI to scale that impact further and faster than anyone else can. About the role: We are seeking an execution-focused and visionary Data Engineering Manager to lead our Data Platform Team. In this critical role, you will manage a team of talented engineers dedicated to building the common, reusable abstractions and high-throughput data infrastructure that power Garner’s internal and external applications. You will champion an AI-native development philosophy and scale our data pipeline capabilities, ensuring our systems seamlessly support the heavy computational demands of our next phase of company-wide growth. Where you will work: This role will be based in our New York City office (in the Financial District). You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday. What you will do: Lead and manage the Data Platform Engineering team, owning the end-to-end design, implementation, and operational excellence of systems that process complex healthcare data Provide high-quality technical contributions and architectural guidance, establishing robust standards for data modeling, processing, and system maintainability Build and optimize common data abstractions, pipelines, and frameworks designed for high scalability, ensuring our platform keeps pace with exponential enterprise data growth Translate complex, ambiguous business and data requirements into well-scoped, accurately estimated, and composable architectural pieces Foster and drive an AI-native engineering culture within the team, integrating cutting-edge AI tools and integrations to enhance development workflows and automate data operations Manage and grow stakeholder relationships across Engineering and Product, aligning team priorities with departmental OKRs and broader business goals Mentor, coach, and guide your team members, conducting detailed design reviews and managing robust Root Cause Analyses (RCA) to continuously elevate technical rigor The ideal candidate has: 5+ years of hands-on engineering experience, with a focus on Big Data and distributed system and infrastructure 2+ years of managing engineering teams, with a track record of building and mentoring high performing teams Expertise with batch data processing frameworks such as Hadoop, Spark, EMR, etc. Experience with stream processing systems such as Kafka and RedPanda Proficiency with technologies such as Temporal, Apache iceberg, AWS glue, Spark, DBT, and Soda Exhibits strong ownership and a proactive mindset, with a proven ability to translate complex business needs into scalable, actionable technical roadmaps Demonstrated ability to operate effectively in dynamic, fast-paced startup environments, with a hands-on approach and a strong commitment to organizational success Preference for managing healthcare data standards (e.g. HIPAA, EDI) A desire to be a part of our mission to improve the U.S. healthcare system A desire to be a part of a high-performing, mission-driven team that operates with intense urgency, a strong sense of individual accountability, and a commitment to authentic feedback Technologies we use: Python, Spark, dbt, SnowFlake, Iceberg, Airflow, OpenMetadata, Kubernetes, NATS, AWS, Terraform, Claude Code This is a unique opportunity to join a fast-growing company in a transformative role, helping shape the future of healthcare. What you'll get here: You’ll work on problems that matter, at a company working to change healthcare at scale. You’ll work at the intersection of AI and systemic healthcare reform, where the problems we solve are as interesting and compelling as the mission. At Garner, you’ll take on real, ambitious problems with real ownership and autonomy, alongside exceptional, principles-based people who genuinely want you to win. It’s demanding by design. You’ll be challenged to stretch beyond what you thought possible and receive consistent coaching to help you grow and do the best work of your career. This isn't the right fit for everyone, and that's intentional. The people here are driven by what's at stake for real people, and that's what gives our intensity its purpose. Compensation Transparency: The target salary range for this position is $310,000 - $385,000. Individual compensation for this role will depend on various factors, including qualifications, skills, and applicable laws. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including but not limited to: flexible PTO, Medical/Dental/Vision plan options, 401(k), Teladoc Health and more. Fraud and Security Notice: Please be aware of recent job scam attempts. Our recruiters use getgarner.com and garnerhealth.com email domains exclusively. If you have been contacted by someone claiming to be a Garner recruiter or a hiring manager from a different domain about a potential job, please report it to law enforcement here and to candidateprotection@garnerhealth.com . Equal Employment Opportunity: Garner Health is proud to be an Equal Employment Opportunity employer and values diversity in the workplace. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics. Garner Health is committed to providing accommodations for qualified individuals with disabilities in our recruiting process. If you need assistance or an accommodation due to a disability, you may contact us at talent@garnerhealth.com .

Location: Warsaw Who are we? Tripledot Studios is one of the largest independent mobile games companies in the world. We are a multi-award-winning organisation, with a global 2,500+ strong team across 12 studios. Our expanded portfolio includes some of the biggest titles in mobile gaming, collectively reaching top chart positions around the world and engaging over 25 million daily active users. Tripledot’s guiding principle is that when people love what they do, what they do will be loved by others. We’re building a company we’re proud of. One filled with driven, incredibly smart and detail-orientated people, who LOVE making games. Our ambition is to be the most successful games company in the world, and we’re just getting started. Key Responsibilities Support Kubernetes and the services deployed on it, including Airflow, AI tools, and MCP servers. Build, maintain, and improve CI/CD pipelines and GitHub setup. Manage AWS infrastructure, including IAM roles, policies, and permissions across multiple accounts. Implement and maintain secrets management. Partner with engineers and analysts to productionize work and support reliable operation at scale. Help shape infrastructure approaches for high-load and high-volume workloads. Required Skills, Knowledge and Expertise Strong hands-on experience with Kubernetes and AWS. Experience supporting services and deployment pipelines in a production environment. Experience with infrastructure in a high-load or high-volume B2C environment, or a similarly demanding setting. Experience managing distributed infrastructure across multiple cloud accounts is a plus. Familiarity with Airflow, GitHub, CI/CD, and secrets management is valuable. Working at Tripledot 25 days paid holiday in addition to bank holidays to relax and refresh throughout the year Hybrid Working 20 days remote working: Work from anywhere in the world, or use the time to cover mandatory office days to WFH, 20 days of the year. Daily Free Lunch: when in the office you get £12 every day to order from JustEat. Regular company events and rewards: quarterly on-site and off-site events that celebrate cultural events, our achievements and our team spirit. Employee Assistance Program: Anytime you need it, tap into confidential, caring support with our Employee Assistance Program, always here to lend an ear and a helping hand. Family Forming Support: Receive vital support on your family forming/ fertility journey with our support program [subject to policy] Life Assurance & Group Income Cover: Financial protection for you and your loved ones. Continuous Professional Development: Propel your career with continuous opportunities for professional development. Private Medical Cover & Health Cash Plan: Opt-in (P11d benefit) comprehensive private medical cover with Bupa and cash plan with Medicash. Dental Cover: Opt-in (P11d benefit). Cycle to Work Scheme: Salary sacrifice bike purchase scheme. Pension Plan: Qualifying earnings or opt-in 4% contributory match schemes offered.

Location: San Francisco, CA | New York City, NY About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role As a Data Engineer on the Data Science & Analytics team, you will build the data foundation for Anthropic’s quote-to-cash lifecycle: the path a deal takes from opportunity through revenue. You will design the canonical data models so that Sales, Deal Desk, Order Management, Revenue Operations and Finance work from one governed, auditable definition of what was sold, on what terms, and where each deal stands. You will partner closely with DS&A and with GTM and Finance systems teams who own Salesforce, CPQ and billing to make quote-to-cash data reliable, well-modeled and self-serve as our business scales. Responsibilities Understand the data needs of Deal Desk, Order Management, Revenue Operations, Finance and Sales systems teams, and translate them into technical requirements Design, build and own data models that transform raw Salesforce, CPQ, and billing data into canonical datasets Establish high data integrity standards and SLAs to ensure timely, accurate delivery of data Partner with Salesforce, CPQ and billing engineers on upstream schema changes, new fields and ingestion so the warehouse faithfully mirrors the systems of record Build foundational data products, dashboards and tools to enable self-serve analytics to scale across GTM teams Influence stakeholder roadmaps from a data perspective, and become the expert on Anthropic’s GTM data models and architecture You might be a good fit if you have 5+ years of experience as a Data Engineer, Analytics Engineer or in a similar Data Science & Analytics role, ideally partnering with GTM, Revenue Operations or Finance teams. A passion for the company's mission of building helpful, honest, and harmless AI. Hands-on experience modeling Salesforce data and at least one adjacent quote-to-cash system: CPQ, contract lifecycle management, billing and invoicing, or ERP. Expertise in building multi-step ETL jobs, building robust data models through tooling like dbt; proficiency with workflow management platforms like Airflow and version control management tools through GitHub. Expertise in SQL and Python to transform data into accurate, clean data models. Experience building data reporting and dashboarding in visualization tools like Hex to serve multiple cross-functional teams. A bias for action and urgency, not letting perfect be the enemy of the effective. A “full-stack mindset”, not hesitating to do what it takes to solve a problem end-to-end, even if it requires going outside the original job description. Experience building an Analytics Data Engineering (or similar) function at start-ups. A strong disposition to thrive in ambiguity, taking initiative to create clarity and forward progress. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $405,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Sofia Xometry (NASDAQ: XMTR) powers the industries of today and tomorrow by connecting the people with big ideas to the manufacturers who can bring them to life. Xometry’s digital marketplace gives manufacturers the critical resources they need to grow their business while also making it easy for buyers at Fortune 1000 companies to tap into global manufacturing capacity. Product Analyst Product Analyst is embedded within a product squad, providing analytical support to grow our two key marketplace platforms: the customer-facing platform and the partner-facing platform. The role involves deep analysis of user behavior, end-to-end analytics, and the planning and analysis of A/B tests. You will work in close collaboration with Product Managers to drive data-driven decisions, with a key focus on optimizing pricing strategies and identifying product growth opportunities. This position works end-to-end: clarifying problems, shaping metrics, partnering with DWH, building usable tools, and supporting adoption. The scope of the role is cross-regional. Tech Stack: Analytics/DWH: SQL, Python, Snowflake, Airflow, Looker, Amplitude, Google Analytics, Google Sheets, DataHub, Git, Statsig, DBT Application Platform: PostgreSQL, Ruby, Salesforce Project/Process Management: JIRA, Miro, Figma AI-Native: Claude, Spec-Driven Development How You’ll Contribute: Experimentation & A/B Testing: Partner with Product Managers and stakeholders to define the problem, success metrics, and hypotheses; propose experiments and measure impact on the business Own product A/B testing and the end-to-end product hypothesis pipeline, from ideation to analysis and implementation Formulate hypotheses for new product features, design A/B experiments, analyze results, and provide recommendations to improve user conversion and engagement Design and analyze A/B tests for different pricing models and strategies to maximize revenue while balancing conversion rate and bookings Analytics & Reporting: Build and maintain dashboards in Looker; ensure correctness of data, clarity, usability, and decision support Analyze product funnels, user behavior, and platform performance (activation, conversion, retention, pricing effects) to drive product growth, revenue, and margin Track lifecycle KPIs, trends, and customer feedback, and recommend actions Conduct in-depth product analysis and uncover insights not covered by self-service analytics Data Quality & Partnership: Specify data needs, validate data quality, and collaborate with DWH on marts and metrics Treat analytics as a product: start with “why,” simplify, and ensure usability and adoption What You’ll Bring to Xometry: Education: Bachelor’s or Master’s in Data Science, Computer Science, IT, Engineering, or a related field Experience: 3+ years in product analytics, ideally within B2B/B2C marketplaces Technical Skills: Strong SQL and Python; experience with BI tools (Looker, Tableau, Qlik, or similar); familiarity with Git, JIRA, Confluence, or equivalents Quantitative Skills: Solid foundations in probability and statistics; hands-on experience with statistical analysis and experimentation (hypothesis testing, A/B test design, power analysis), regression, and time-series analysis Nice to Have: Understanding of DWH concepts (design, data structures, ETL/ELT) and applied experience with Statsig or another A/B testing platform Soft Skills: Strong business sense, clear communication, structured thinking, attention to data quality, and the ability to decompose ambiguous asks into deliverables Curiosity: A track record of self-directed development in adjacent disciplines such as entrepreneurship, business administration, product, UX, machine learning, or human behavior and psychology is a strong plus Languages: Fluent English with strong communication skills is required; Russian is a big plus; German or another EU language is a plus Location: Sofia, Bulgaria Please apply with an English CV #LI-Hybrid #Promoted Xometry is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status. Please read our privacy policy here.

Location: Munich Xometry (NASDAQ: XMTR) powers the industries of today and tomorrow by connecting the people with big ideas to the manufacturers who can bring them to life. Xometry’s digital marketplace gives manufacturers the critical resources they need to grow their business while also making it easy for buyers at Fortune 1000 companies to tap into global manufacturing capacity. Product Analyst Product Analyst is embedded within a product squad, providing analytical support to grow our two key marketplace platforms: the customer-facing platform and the partner-facing platform. The role involves deep analysis of user behavior, end-to-end analytics, and the planning and analysis of A/B tests. You will work in close collaboration with Product Managers to drive data-driven decisions, with a key focus on optimizing pricing strategies and identifying product growth opportunities. This position works end-to-end: clarifying problems, shaping metrics, partnering with DWH, building usable tools, and supporting adoption. The scope of the role is cross-regional. Tech Stack: Analytics/DWH: SQL, Python, Snowflake, Airflow, Looker, Amplitude, Google Analytics, Google Sheets, DataHub, Git, Statsig, DBT Application Platform: PostgreSQL, Ruby, Salesforce Project/Process Management: JIRA, Miro, Figma AI-Native: Claude, Spec-Driven Development How You’ll Contribute: Experimentation & A/B Testing: Partner with Product Managers and stakeholders to define the problem, success metrics, and hypotheses; propose experiments and measure impact on the business Own product A/B testing and the end-to-end product hypothesis pipeline, from ideation to analysis and implementation Formulate hypotheses for new product features, design A/B experiments, analyze results, and provide recommendations to improve user conversion and engagement Design and analyze A/B tests for different pricing models and strategies to maximize revenue while balancing conversion rate and bookings Analytics & Reporting: Build and maintain dashboards in Looker; ensure correctness of data, clarity, usability, and decision support Analyze product funnels, user behavior, and platform performance (activation, conversion, retention, pricing effects) to drive product growth, revenue, and margin Track lifecycle KPIs, trends, and customer feedback, and recommend actions Conduct in-depth product analysis and uncover insights not covered by self-service analytics Data Quality & Partnership: Specify data needs, validate data quality, and collaborate with DWH on marts and metrics Treat analytics as a product: start with “why,” simplify, and ensure usability and adoption What You’ll Bring to Xometry: Education: Bachelor’s or Master’s in Data Science, Computer Science, IT, Engineering, or a related field Experience: 3+ years in product analytics, ideally within B2B/B2C marketplaces Technical Skills: Strong SQL and Python; experience with BI tools (Looker, Tableau, Qlik, or similar); familiarity with Git, JIRA, Confluence, or equivalents Quantitative Skills: Solid foundations in probability and statistics; hands-on experience with statistical analysis and experimentation (hypothesis testing, A/B test design, power analysis), regression, and time-series analysis Nice to Have: Understanding of DWH concepts (design, data structures, ETL/ELT) and applied experience with Statsig or another A/B testing platform Soft Skills: Strong business sense, clear communication, structured thinking, attention to data quality, and the ability to decompose ambiguous asks into deliverables Curiosity: A track record of self-directed development in adjacent disciplines such as entrepreneurship, business administration, product, UX, machine learning, or human behavior and psychology is a strong plus Languages: Fluent English with strong communication skills is required; Russian is a big plus; German or another EU language is a plus Location: Munich, Germany Please apply with an English CV #LI-Hybrid #Promoted Xometry is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status. Please read our privacy policy here.

Location: London, United Kingdom Workplace: none About InvestEngine We’re a ~200-person, AI-first fintech. We move fast, we expect people to own things end to end, and we don’t have a lot of specialist support infrastructure to hide behind — if you spot a gap, you’re expected to close it, not escalate it and wait. About the role We’re hiring our first dedicated Analytics Engineer at InvestEngine. This is a foundational hire: you’ll sit at the intersection of data engineering, analytics engineering, and business partnership — turning raw, disparate data into governed, trustworthy models that the rest of the company makes decisions on. You won’t just write dbt models in isolation. You’ll work directly with stakeholders across the business — Product, Engineering, Risk, Finance, Investment, Marketing — to understand the problems they’re actually trying to solve, then design and ship the pipelines, models, and semantic layer that solve them. You’ll also help shape how we use AI-enabled tooling to make the whole analytics function faster, and how we structure our data so that AI tooling can be trusted to answer questions about it. We’re actively modernising our data stack, and we want the person in this role to shape it. Some of it is in place, some of it is being rebuilt, and some of it hasn’t been decided yet. If you have informed opinions about orchestration, ingestion, testing, semantic layers, or BI — we want to hear them. Who thrives here We’ve pitched this at mid to senior level , but the honest position is that the level matters less than the appetite. The ultimate test is hunger, and the desire to shape this space and have real impact . If you’re technically strong but hungrier than your years of experience suggest, apply anyway — we’d rather have someone who wants to own this than someone who has simply done it before somewhere bigger. Beyond that, this role suits someone who: Operates well with ambiguity and limited structure. You’ll be the first analytics engineer here — no existing playbook, no dedicated data engineering team to lean on, no established BI function to slot into. You’ll be building a lot of it as you go. Wants a small, fast company on purpose — not just escaping bureaucracy elsewhere, but genuinely drawn to the trade-off: more ownership and visibility, less process and hand-holding. Lives in the tools, not in email chains. You default to GitHub, dbt, Slack/Notion, and automation over status meetings and Word docs. Can point to things they’ve actually built and shipped , not just designs or concepts they contributed to. Already uses AI to do the job better — Claude, Copilot, or similar — and is curious about where it can go further. Learns fast. We care more about how quickly you close a knowledge gap than how complete your CV looks today. Works well directly with Product, Marketing, Operations, and Engineering , not just with other analysts — this role sits right at that boundary. Our values Everyone at InvestEngine is expected to live these, and this role will be assessed against all five: Act like an owner — you take responsibility for outcomes, not just tasks Keep improving. Stay curious. — you push your own standards and the team’s, and you’re genuinely curious about better ways to work Achieve more together — you make the people around you (business stakeholders included) more effective, not just yourself Speak up. Share openly. — you flag risks, disagreements, and better ideas rather than sitting on them Put customers first. Create real value. — you build things because they solve a real problem, not because they’re interesting to build What you’ll do Analytics engineering & the semantic layer Design, build, and maintain dbt Core models that transform raw data into clean, well-tested, well-documented datasets Own and evolve our semantic layer — consistent metric definitions, business logic, and naming that the whole company can trust and query against Establish and enforce data modelling standards, testing practices, and documentation as the function scales Evaluate and help roll out modern BI tooling — experience with Lightdash, Omni, Looker, or Metabase all translates well — to make metrics genuinely self-serve for non-technical stakeholders Building the AI context layer Structure our models, metric definitions, documentation, and lineage so they’re machine-readable, not just human-readable — because our semantic layer is what AI tooling reasons over Make AI-driven analytics reliable: if two people (or two agents) ask the same business question, they should get the same answer, because the definition lives in one governed place Treat naming, documentation, and metric definitions as first-class engineering deliverables rather than afterthoughts, since they directly determine how trustworthy our AI-enabled BI is Use Claude to accelerate development — model scaffolding, code review, documentation, QA, and pipeline debugging — and Notion AI to keep documentation and runbooks current and genuinely useful Bring a point of view on where AI can responsibly speed up analytics engineering, and help the wider team adopt it well Data engineering & the platform Build and maintain ingestion and transformation pipelines across our stack. We use a modern data stack, such as AWS, Redshift, and Airflow Write production-quality Python for extraction, transformation, and automation tasks that fall outside dbt’s remit Help us modernise: we’re actively evolving this stack and expect you to challenge and improve it, not just operate it Warehouse cost & performance Own the performance and cost profile of our Redshift warehouse — you should be able to read a query plan, choose sensible sort and distribution keys, and design incremental models without being asked Spot and act on opportunities to reduce complexity and cost across our models and pipelines, rather than letting spend and technical debt accumulate quietly Make deliberate trade-offs between freshness, cost, and complexity — and explain those trade-offs to the business in terms they care about Data quality & observability Champion analytics engineering best practice across the company: testing, version control, code review, CI, and documentation applied to data the same way engineering applies them to software Build and own data quality, freshness, and pipeline monitoring so problems are caught by us before they’re caught by a stakeholder looking at a dashboard Set the standard for what “trustworthy data” means here, and hold the line on it as the volume of models and requests grows Upstream influence Work with our engineering teams to make sure application and event design accommodates reporting and analytics requirements before data reaches the warehouse — good analytics starts at the source, not in the transformation layer Influence payload and schema design for new services and product features, so we’re not permanently reverse-engineering business meaning out of operational tables Support analysts and business users with automation, tooling, and data engineering expertise, and provide training and guidance on how to use our data well Business partnership Work directly with stakeholders to understand their problems, not just their requested outputs — translate ambiguous business questions into pipeline and modelling requirements Act as a trusted advisor on what’s possible with our data, and push back constructively when a request doesn’t solve the underlying problem Support financial, operational, and regulatory reporting needs appropriate to a regulated investment platform Governance Apply our data classification and access model when onboarding sources: identify PII (including in free-text fields), decide what belongs in the sanitised analytics layer versus a restricted dataset, and document the outcome Implement least-privilege access across datasets, reports, and dashboards What we’re looking for Must-haves Hands-on production experience with dbt (we run dbt Core) — this is a hard requirement Strong SQL expertise Solid Python for data engineering and automation Working knowledge of AWS data services Experience with GitHub and GitHub Actions (or equivalent CI/CD tooling) for testing and deploying data code Experience with a modern BI tool (e.g., Lightdash, Omni, Looker, or Metabase), and a genuine interest in semantic layers (e.g., MetricFlow, Cube Core) and self-serve analytics Comfortable using AI tools as part of your day-to-day engineering workflow Strong communication — you can sit with a non-technical stakeholder, understand their real problem, and explain trade-offs in plain language Ownership: you’re comfortable being the first and only person in this role, and building things properly from scratch Nice-to-haves Airflow or similar orchestration tools, and familiarity with data ingestion tools such as dlt or Airbyte Warehouse cost and performance tuning, particularly on Redshift Fintech, investment management, or another regulated industry Handling PII responsibly in an analytics environment — sanitised vs. restricted datasets, role-based access tiers, automated PII detection Experience with Notion AI or Asana automations Day-to-day working style We favour clear thinking and simple, well-tested solutions over cleverness We expect you to engage directly with the business, not hide behind tickets — understanding the “why” behind a request is part of the job We’re genuinely trying to use AI to work faster and better, not as a buzzword — we want people who’ll experiment, share what works, and level up the team As the first analytics engineer, you’ll define what “good” looks like here: process, standards, and tooling included We believe data should be as accessible as it can safely be, and as restricted as it needs to be — good judgement applied consistently, not blanket lockdown Practical details Reports to: Chief Data Officer Team: Data & Analytics Level: Mid to Senior Location / working pattern: London / hybrid