Дата-инженер вакансии - в офисе

• Хорошее владение SQL и PL/SQL.

Задачи: — Проектирование, создание и поддержка надежных и масштабируемых ETL-процессов для обработки больших объемов данных. — Разработка и оптимизация SQL-запросов для извлечения и анализа данных. — Сотрудничество с командами аналитиков и разработчиков для определения требований к данным и обеспечению их доступности. — Участие в проектировании архитектуры данных и внедрении новых технологий. — Обеспечение качества данных и их интеграции из различных источников. Требования: — Хорошее владение SQL и PL/SQL; — Владение Python, Go; — Опыт работы с Apache Kafka.

Разработка и поддержка ETL/ELT пайплайнов на Python. Интеграция данных из различных источников (API, базы данных, файлы, облачные хранилища).

Ваши обязанности: Разрабатывать и поддерживать ETL-процессы для загрузки данных в нашу DWH на Vertica; Проектировать и развивать витрины данных, чтобы аналитики и бизнес-пользователи получали точные и своевременные метрики;

• Architect on‑prem & cloud data platforms. • Build data ingestion, pipelines & ETL/ELT.

Location: United States; Canada Workplace: OnSite WHO ARE WE? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Role Overview As a Member of Technical Staff in Post-Training Data Infrastructure, you will build and own the pipelines that produce the post-training data for our large language models (LLMs), from SFT and RL data to synthetic and human-annotated data. You will work closely with researchers and annotation teams so they can iterate on data as quickly as they iterate on models. Key Responsibilities - Build and maintain data pipelines for SFT, preference, and RL post-training data. - Own dataset quality, versioning, and reproducibility. - Turn research data recipes into production-grade, reusable components. - Scale model inference for data generation and model-based grading. - Design data formats and schemas that keep post-training datasets consistent and efficient to train on. - Collaborate with researchers and engineers to make sure data pipelines keep pace with the needs of cutting-edge language models. Qualifications - Strong software engineering skills. - Experience designing and building large-scale data pipelines for machine learning, ideally for LLM post-training. - Track record of running distributed data processing and model inference at scale. - Good understanding of post-training methods (e.g., SFT, RLHF, DPO) and how data shapes model behaviour. - Excellent communication skills. This is neither an exhaustive nor necessary set of attributes. Even if none of these apply to you, but you believe you will contribute to Cohere, please reach out. We value diverse backgrounds and perspectives at Cohere   FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS: - A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch. - Full health and dental benefits, including a separate budget for mental health. - RRSP matching, 401K, Pension Scheme. - 100% Parental Leave top-up for up to 6 months, for either parent. - Annual enrichment benefits: Arts & culture, fitness/wellness, quality time, and a workspace improvement credit. Education & learning stipend for conferences, courses, and coaching. - 6 weeks of paid vacation (30 working days!) - Budget for traveling to other offices if you are remote, plus an annual company offsite. HOW AND WHERE WE WORK: - Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon. - For those in the office: a daily lunch program, plenty of snacks, and regular community and social events. - For those not near an office: a co-working benefit so you can work alongside others in your city. - Everyone receives a $500 home office stipend to set up your workspace properly. If any of the above doesn’t line up exactly with your experience, we still encourage you to apply. We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs. We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider. Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.

Обязанности: — Ведение делопроизводства редакции (табель, служебная документация и т.п.). — Помощь редакторам в подготовке и сдаче книг. — Работа с базой 1С. — Ведение проектов в 1С. — Внесение данных и изменение в программе учета 1С. — Формирование отчетов из программ учета по запросам руководителей. — Распечатка/пересылка рукописи для рецензента/редактора и др. — Ксерокопирование документов. — Информирование об отказе рукописи. — Подготовка и предоставление комплекта сопроводительных документов для производства рукописи. — Внесение изменения в сопроводительные документы. — Внутренняя сверка макетов, проверка колонтитулов и колонцифр (помощь редакторам). — Составление презентаций для продвижения книг. — Осуществление взаимодействия с другими редакциями и службами. — Сдача допечаток. — Отслеживание информации о запрещенных веществах и лгбт-контента, при необходимости внесение информации в 1С, выходные данные и обложку книги. Требования: — Высшее/Среднее специальное образование (в области издательского дела). — Знание основ полиграфического производства, полиграфических материалов и издательских процессов. — Уверенный пользователь ПК: Word, Excel, PowerPoint, желательно 1С. — Организованность, стрессоустойчивость, многозадачность, коммуникабельность. Условия: — Полная пятидневная рабочая неделя (10.00 - 18.30) в офисе в Москва-Сити, Башня "Империя" (метро и МЦК "Деловой центр", метро Москва-Сити). — После испытательного срока один удаленный день. — Оклад, премирование по системе KPI после испытательного срока. — Соблюдение ТК РФ. — Социальный пакет: добровольное медицинское страхование и страхование выезжающих за рубеж, программы заботы о здоровье, занятия спортом, значительная скидка на книги и другую продукцию издательства. — Обучение и профессиональное развитие, внутренние и внешние курсы и тренинги. — Публичная оценка выдающихся результатов и призы для победителей на корпоративной Премии «Признание». — Планирование карьеры в Издательской группе. — Справедливое вознаграждение и премирование по системе KPI.

Location: New York; San Francisco Workplace: OnSite ABOUT US: AI NEEDS A NEW INFRASTRUCTURE LAYER. WE'RE BUILDING IT AT MODAL. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation. Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. ABOUT GTM ENGINEERING: GTM Engineering is responsible for the data, business logic, workflows, and agents that enable our Marketing and Sales teams to help customers get the most out of Modal. Like the infrastructure beneath the Magic Kingdom https://en.wikipedia.org/wiki/Disney_utilidor_system, much of our work happens behind the scenes. We build systems that anticipate and deliver what customers need to grow on Modal. We bring a product management, growth engineering, and data science approach to GTM, from centralizing our data platform https://deepline.com/blog/gtm-ai-nyc-chris-modal to building agents for account research https://parallel.ai/blog/case-study-modal. THE ROLE: We’re looking for a GTM Engineer to own our GTM data platform. This role combines analytics engineering with revenue operations: you’ll build the org map, people graph, and data systems that help us discover future customers and support our GTM workflows, partnering closely with our Data team on the shared foundation. WHAT YOU’LL WORK ON: - Build and maintain an org map and people graph connecting companies, labs, teams, and individuals to identify researchers, engineers, and early-stage startups, including people beyond our existing users and CRM - Own identity resolution, enrichment, and CRM schemas so affiliations, roles, and relationships stay accurate, current, and traceable to their sources - Build GTM data models in Snowflake and dbt connecting people, companies, workspaces, workloads, usage, and revenue, giving reps and agents a shared foundation for customer context - Partner with RevOps and Marketing to define and implement metrics, data models, and business rules for routing, territory assignments, compensation, pipeline reporting, and attribution - Partner with Data on usage forecasts and account signals; build reproducible reporting that reconciles actuals, forecasts, human adjustments, and credited attainment - Build reverse ETL pipelines for ad audiences, lifecycle campaigns, CRM enrichment, outbound, and event outreach, preserving engagement history and campaign eligibility - Own data quality and system health, from graph coverage and stale enrichment to failed syncs and historical reporting accuracy - Build the tooling that helps GTM Engineering ship reliably, from development environments, automated tests, and deployments to backups and shared AI agents and skills YOU MAY BE A FIT IF: - You’re fluent in SQL and proficient in Python - You already use AI coding tools and agents to build, debug, and automate your work, and help teammates do the same - You’ve built data models and production pipelines with tools like Snowflake, dbt, Modal, or Hex - You have hands-on experience with CRM architecture, integrations, and revenue or marketing operations - You understand how leads, accounts, opportunities, and revenue move through a business - You have high attention to detail and a habit of testing, documenting, and monitoring the systems you build NICE-TO-HAVE: - Experience with usage-based businesses

• Уверенное знание SQL и практический опыт работы с данными. • Практический опыт Data Processing и Data Analysis.

• Опыт от 4 лет с Java/Scala, Apache Flink (или Beam/Spark Streaming) и Kafka.

• Проектирование и развитие аналитического хранилища данных (DWH). • Разработка моделей данных (fact / dimension, агрегаты).

Опыт от 4 лет с Java/Scala, Apache Flink (или Beam/Spark Streaming) и Kafka. Знание событийно-ориентированной архитектуры и потоковой обработки данных.

We’re looking for an experienced Data Engineer with strong Java/Scala, Apache Flink, and Kafka skills to build and optimize real-time data streaming solutions in an enterprise environment. 💼 4+ years of experience with Java/Scala, Flink (or Beam/Spark Streaming), and Kafka.

We’re looking for an experienced Data Engineer with strong Java/Scala, Apache Flink, and Kafka skills to build and optimize real-time data streaming solutions in an enterprise environment. 💼4+ years of experience with Java/Scala, Flink (or Beam/Spark Streaming), and Kafka.

Wincent is looking for a Data Platform Engineer to improve data accessibility, ensure data quality, and enhance cloud-based data processing.

Проектирование, разработка и сопровождение ETL/ELT-процессов с использованием Python и Apache Airflow. Практический опыт эксплуатации Apache Airflow, Spark (PySpark),ClickHouse и PostgreSQL в промышленной среде.

-Проектирование и сопровождение БД: разработка и поддержка объектов базы данных (таблицы, представления, индексы, хранимые процедуры) в среде Greenplum. -Разработка витрин данных: проектирование и расчет показателей для витрин данных под бизнес-задачи с использованием SQL.

Задачи: — Проектировать и разрабатывать масштабируемые ETL/ELT-процессы для загрузки и трансформации данных в корпоративное хранилище (DWH) на базе стека Oracle; — Писать и оптимизировать сложные запросы SQL и хранимые процедуры PL/SQL; — Участвовать в проектировании моделей данных (Data Vault, Star/Snowflake schema) совместно с архитекторами и аналитиками; — Обеспечивать качество данных (Data Quality), мониторить пайплайны и оперативно устранять инциденты; — Взаимодействовать с командами Data Science и BI-аналитиками для предоставления витрин данных. Требования: — Опыт работы в роли Data Engineer / ETL Developer / Oracle Developer от 3-х до 5 лет; — Глубокое знание экосистемы Oracle; — Отличное знание SQL и PL/SQL (умение писать и оптимизировать сложный код); — Понимание принципов построения хранилищ данных (DWH) и моделей данных; — Английский язык на уровне Upper-Intermediate (B2) и выше (рабочий язык в международной команде и для общения с заказчиком); — Готовность к релокации в г. Маскат, Оман и работе в офисе. Условия: — Локация: г. Маскат, Оман (офисный формат работы); — Длительность проекта: 7 месяцев; — Полное сопровождение релокации со стороны компании: оформление рабочей визы, медицинская страховка, оплачиваемый перелет и помощь с организацией переезда.