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Location: New York Workplace: remote ⚡️ About Merkle Science Merkle Science provides blockchain transaction monitoring and intelligence solutions for web3 companies, digital asset service providers, financial institutions, law enforcement and government agencies to detect, investigate and prevent illicit use of cryptocurrencies. Our vision is to make cryptocurrencies safe and provide infrastructure for the safe and compliant growth of cryptocurrencies. Merkle Science is headquartered in New York with offices in Singapore, Bangalore and London. The team has combined experience across Bank of America, Paypal, Luno, Thomson Reuters and Amazon. The company has raised over US$27 MM from SIG, Beco, Republic, DCG, Kenetic, GGV and several others. The Role You will design, build and run the systems behind our data products, covering ingestion, processing, analytical storage and serving. You own your work from the design document through to production. The people who design a pipeline also run it and answer for its numbers. Our stack is very wide, and we go deep in every part of it. We don't stop at "it works." We keep measuring, profiling and tuning to make systems faster, cheaper and more reliable. We want engineers who are razor sharp, pay close attention to detail, and care as much about why a query takes 800 ms as whether it returns the right answer. You don't need blockchain experience. If you're a strong data engineer who wants to learn it, we'd like to hear from you. What You'll Do Design and build scalable, high-throughput data pipelines and platform systems for blockchain data across 30+ chains. Build backend data services and loaders in Go and Python. Design and evolve data models and analytical workloads in ClickHouse, and tune them for sub-second queries. Build incremental, idempotent pipelines on Databricks over billions of rows and tens of terabytes. Keep improving the scale, reliability, freshness, performance and cost efficiency of the platform. Investigate hard performance and reliability problems and see them through to resolution. Work closely with the product, analytics and investigations teams that use the data, and turn their needs into platform designs. Help set technical direction through architecture discussions, design reviews and code reviews. Mentor engineers and help set strong engineering and operational practices. What You Bring 5+ years building and operating production data systems, including ETL and ELT pipelines. At least two of those years should be spent owning systems that other engineers depend on. Expert Python and strong SQL. You write typed, tested code, are comfortable with asyncio and concurrency, and have built incremental data models over billions of rows. Go for backend services. You have built and run high-throughput services or data loaders in Go in production. ClickHouse in production. You know MergeTree schema and sort-key design, materialised views, bulk loads and query tuning. Databricks in production. You have worked with PySpark, Delta Lake and Unity Catalog, designed incremental and idempotent tables, and kept an eye on compute cost. Large-scale data. You have hands-on experience with pipelines over billions of rows and tens of terabytes. You understand how partitioning, clustering and file sizing affect cost and latency. Kafka, Airflow and Kubernetes in production. You understand delivery semantics and idempotent writes, have handled backfills and failures, and have run data workloads with Docker and Helm. Google Cloud is preferred; AWS is fine. An operations and communication mindset. You instrument what you build, write runbooks and post-incident reviews, and can write a design document that a reviewer in another time zone can approve.   Nice to Have Blockchain data: EVM, UTXO or non-EVM chain data models, and pulling data from nodes over JSON-RPC or gRPC. Graph or distributed key-value stores: entity resolution or clustering on large graphs. Infrastructure as code, GitOps and observability: Terraform, Helm, ArgoCD, OpenTelemetry, Prometheus and Grafana, and load-test design. Experience building platforms that serve multiple data sources, datasets or product teams. AI-assisted development, and experience in fintech, compliance or AML. How We Work You'll join a small, senior team where everyone owns their work end to end. We make decisions based on measured evidence, not opinion. We keep design notes and runbooks next to the code. We test at production scale and run blameless post-incident reviews. We use AI coding tools every day, and we use judgement when reviewing what they produce. ❤️ Well Being, Compensation and Benefits We care about your well-being. Along with excellent health insurance, we offer flexible time off, learning & development initiatives and hours that are designed to provide work/life balance.  We regularly host team-building sessions and encourage discussions around mental health.   We reward talent and believe in acknowledging people for their contributions.  We offer industry-leading compensation, along with generous equity.  As a rapidly growing business, there are endless opportunities to grow your career with Merkle Science.

🧩 Требования: промышленный опыт от 5 лет в data engineering или backend-разработке с сильным дата-уклоном — production-системы под реальной нагрузкой и ответственность за их эксплуатацию;

Location: Intellias Meet your recruiter Kateryna Zemetska kateryna.zemetska@intellias.com https://www.linkedin.com... Vacancy details Data Engineering Data Engineer Senior Bulgaria, Croatia, Poland, Portugal, Spain, Ukraine Remote APPLY NOW REFER A FRIEND We are hiring a Senior Data Engineer to join an Intellias delivery team on a large-scale enterprise data platform migration programme for a financial services client. This is a hands-on senior role. You will write production code every week, own architectural decisions on your workstream, and mentor a paired mid-level engineer. What project we have for you Our client is an independent, active global asset manager with over R3 trillion in assets under management. They are executing a firm-wide data strategy to govern, manage and engineer data as a product across more than 70 business-owned sub-domains. As part of this, they are migrating to Databricks as the foundational layer of their Enterprise Data Platform, adopting a lakehouse architecture built on open formats, declarative pipelines and Unity Catalog. What you will do Perform the end-to-end delivery of one or more priority data domains on Azure Databricks. Design and build medallion (Bronze / Silver / Gold) pipelines using Lakeflow Declarative Pipelines, Auto Loader, Structured Streaming and Delta Lake with Liquid Clustering. Register curated data products in Unity Catalog with the correct tags, masks, row filters, lineage and access policies. Implement data quality gates at the Silver-to-Gold boundary, and refine rules with domain stewards. Package and deploy work using Databricks Asset Bundles through Azure DevOps CI/CD Write automated tests and documentation Work at AI-as-Collaborator level today with a path to AI-as-Orchestrator, using Claude Code, Databricks Assistant, GitHub Copilot and MCP-connected agent tooling to accelerate the recurring parts of pipeline build, test and deploy. What you need for this 6+ years of data engineering experience, with at least the last three years on Databricks in production. Deep hands-on experience with medallion / Lakehouse architecture, Delta Lake, Unity Catalog, Lakeflow Declarative Pipelines and Databricks Asset Bundles. Strong python, PySpark and SQL, comfortable diagnosing performance issues on large workloads through Spark UI and Photon. Real production experience with streaming ingestion (Kafka Structured Streaming, Auto Loader) and CDC patterns. Experience implementing data quality at scale (Great Expectations or equivalent), plus lineage, cataloguing and access control. CI/CD on data platforms through Azure DevOps or GitHub Actions, with infrastructure as code (Terraform or equivalent). Fluent working English. Nice to have Active Databricks certifications Prior exposure to investment management platforms, asset management operations data, market data feeds from major providers CDMP DAMA certification or equivalent data governance credential. Experience applying agentic engineering tooling to production data engineering (not just personal productivity), including MCP-connected Databricks or cloud servers. What it’s like to work at Intellias At Intellias, where technology takes center stage, people always come before processes. By creating a comfortable atmosphere in our team, we empower individuals to unlock their true potential and achieve extraordinary results. That’s why we offer a range of benefits that support your well-being and charge your professional growth. We are committed to fostering equity, diversity, and inclusion as an equal opportunity employer. All applicants will be considered for employment without discrimination based on race, color, religion, age, gender, nationality, disability, sexual orientation, gender identity or expression, veteran status, or any other characteristic protected by applicable law. We welcome and celebrate the uniqueness of every individual. Join Intellias for a career where your perspectives and contributions are vital to our shared success. Skills Databricks

Workplace: Удаленно RWB — международная технологическая группа компаний, образованная в результате слияния двух лидеров рынка — IT-компании Wildberries и оператора наружной рекламы Russ. Мы развиваем одну из крупнейших цифровых экосистем, объединяя электронную коммерцию, финтех, логистику, рекламу, путешествия, здоровье и другие направления. Наши технологии помогают людям решать повседневные задачи, а предпринимателям — находить новые возможности для роста. Портал Продавцов — это личный кабинет продавца, где он отслеживает свои товары, изучает аналитику и руководит процессами продаж. Логистика, динамическое ценообразование, подъем-моделирование, рекомендации, временные ряды, качественные данные и NLP — мы работаем во множестве доменов. Наша задача — не только обеспечить продавцу принятие решений по данным, но и вывести весь UX продавца на новый уровень: от распределения товаров по складам до ИИ для создания карточек товара. Сейчас мы ищем новых коллег ML Engineer'ов, чтобы вместе внедрять SOTA-модели и растить бизнес-метрики на миллиарды. Обязанности Строить и реализовывать модели классического ML/DL; Проводить анализ используемых моделей и улучшать их; Использовать и понимать современные подходы e2e ML-разработки; Уметь запускать инференс моделей: batch и real-time; Принимать участие в формулировке в продуктовой постановке задачи; Принимать участие в дизайне A/B экспериментов; Проактивно взаимодействовать с аналитиками и менеджерами по продуктам. Требования Опыт решения задач классического ML (временные ряды, классификация, регрессия); Уверенный опыт разработки на Python (не менее 2-х лет); Опыт работы с Scikit-learn, PyTorch, бустингами; Опыт написания backend-оберток для ML-моделей на FastAPI; Навыки контейнеризации k8s будут плюсом; Опыт работы с Apache Airflow, ClearML или аналогичными решениями; Опыт в Big Data стеке: HDFS, PySpark, Trino; Умение использовать эконометрические подходы в работе с данными. 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.

Location: Bangalore Workplace: hybrid ⚡️ About Merkle Science Merkle Science provides blockchain transaction monitoring and intelligence solutions for web3 companies, digital asset service providers, financial institutions, law enforcement and government agencies to detect, investigate and prevent illicit use of cryptocurrencies. Our vision is to make cryptocurrencies safe and provide infrastructure for the safe and compliant growth of cryptocurrencies. Merkle Science is headquartered in New York with offices in Singapore, Bangalore and London. The team has combined experience across Bank of America, Paypal, Luno, Thomson Reuters and Amazon. The company has raised over US$27 MM from SIG, Beco, Republic, DCG, Kenetic, GGV and several others. The Role You will design, build and run the systems behind our data products, covering ingestion, processing, analytical storage and serving. You own your work from the design document through to production. The people who design a pipeline also run it and answer for its numbers. Our stack is very wide, and we go deep in every part of it. We don't stop at "it works." We keep measuring, profiling and tuning to make systems faster, cheaper and more reliable. We want engineers who are razor sharp, pay close attention to detail, and care as much about why a query takes 800 ms as whether it returns the right answer. You don't need blockchain experience. If you're a strong data engineer who wants to learn it, we'd like to hear from you. What You'll Do Design and build scalable, high-throughput data pipelines and platform systems for blockchain data across 30+ chains. Build backend data services and loaders in Go and Python. Design and evolve data models and analytical workloads in ClickHouse, and tune them for sub-second queries. Build incremental, idempotent pipelines on Databricks over billions of rows and tens of terabytes. Keep improving the scale, reliability, freshness, performance and cost efficiency of the platform. Investigate hard performance and reliability problems and see them through to resolution. Work closely with the product, analytics and investigations teams that use the data, and turn their needs into platform designs. Help set technical direction through architecture discussions, design reviews and code reviews. Mentor engineers and help set strong engineering and operational practices. What You Bring 5+ years building and operating production data systems, including ETL and ELT pipelines. At least two of those years should be spent owning systems that other engineers depend on. Expert Python and strong SQL. You write typed, tested code, are comfortable with asyncio and concurrency, and have built incremental data models over billions of rows. Go for backend services. You have built and run high-throughput services or data loaders in Go in production. ClickHouse in production. You know MergeTree schema and sort-key design, materialised views, bulk loads and query tuning. Databricks in production. You have worked with PySpark, Delta Lake and Unity Catalog, designed incremental and idempotent tables, and kept an eye on compute cost. Large-scale data. You have hands-on experience with pipelines over billions of rows and tens of terabytes. You understand how partitioning, clustering and file sizing affect cost and latency. Kafka, Airflow and Kubernetes in production. You understand delivery semantics and idempotent writes, have handled backfills and failures, and have run data workloads with Docker and Helm. Google Cloud is preferred; AWS is fine. An operations and communication mindset. You instrument what you build, write runbooks and post-incident reviews, and can write a design document that a reviewer in another time zone can approve.   Nice to Have Blockchain data: EVM, UTXO or non-EVM chain data models, and pulling data from nodes over JSON-RPC or gRPC. Graph or distributed key-value stores: entity resolution or clustering on large graphs. Infrastructure as code, GitOps and observability: Terraform, Helm, ArgoCD, OpenTelemetry, Prometheus and Grafana, and load-test design. Experience building platforms that serve multiple data sources, datasets or product teams. AI-assisted development, and experience in fintech, compliance or AML. How We Work You'll join a small, senior team where everyone owns their work end to end. We make decisions based on measured evidence, not opinion. We keep design notes and runbooks next to the code. We test at production scale and run blameless post-incident reviews. We use AI coding tools every day, and we use judgement when reviewing what they produce. ❤️ Well Being, Compensation and Benefits We care about your well-being. Along with excellent health insurance, we offer flexible time off, learning & development initiatives and hours that are designed to provide work/life balance.  We regularly host team-building sessions and encourage discussions around mental health.   We reward talent and believe in acknowledging people for their contributions.  We offer industry-leading compensation, along with generous equity.  As a rapidly growing business, there are endless opportunities to grow your career with Merkle Science.

Location: Москва Мы ищем в команду аналитика, который будет формировать и тестировать гипотезы по портфелю корпоративных клиентов, работающих в области сырьевых товаров (металлы, нефть, химия, лес). В управлении второй по объему портфель в Сбере. Ключевые задачи команды : \- разработка стратегий работы (включая цифровые) с корпоративными клиентами бизнес сегмента "средний+" \- мониторинг качества кредитного портфеля, анализ причин портфельных изменений, формирование предложений по результатам анализа. Обязанности * сбор данных для проведения портфельного риск-менеджмента с учетом отраслевой специфики сырьевых отраслей * подготовка аналитических материалов и отчетов о финансовом состоянии заемщиков портфеля * портфельный анализ с целью оценки эффективности текущих стратегий выдач * сбор данных для оценки бизнес-инициатив * расчет эффекта от бизнес-инициатив * исследование источников данных, проверка признаков для целей моделирования * поиск и анализ нетиповых гипотез/корреляций * формирование регулярной отчетности по направлению кредитных рисков * подготовка бизнес -ребований на доработку витрин данных * тестирование прототипов витрин данных * ad hoc аналитика. Требования * опыт в финансовой организации от 3-х лет * высшее математическое или техническое образование * знание принципов и инструментов работы с большими данными SQL/Python * знание основных портфельных риск-метрик в идеале - знания статистики, принципов мат. моделирования, концепции статистической проверки гипотез, построения доверительных интервалов, регрессионного анализа. Будет плюсом: * PySpark * классический ML * работа с корпоративными хранилищами данных (Hadoop Hive) * понимание основных принципов построения финансовой отчетности юридического лица. Условия * комфортный современный офис рядом с м. Кутузовская * ежегодный пересмотр зарплаты, годовую премию * корпоративный спортзал и зоны отдыха * уникальную систему обучения Сбера для профессионального и карьерного развития * расширенный ДМС и льготное страхование для семьи * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * бесплатную подписку СберПрайм+, скидки на продукты компаний-партнеров * вознаграждение за рекомендацию друзей в команду Сбера * корпоративную пенсионную программу.

Location: Хабаровск Команда внутреннего аудита анализирует внутренние ИТ-процессы и решения компании и помогает Сберу стать лучше. Сочетаем глубокую экспертизу и современные методы обработки данных. Ищем в команду начинающих и опытных специалистов, которые хотят построить карьеру в аудите и развиваться в одной из крупнейших технологических компаний. Обязанности * участие в проведении аудита ИТ-систем Банка * разработка алгоритмов и инструментов анализа данных * формирование и отработка гипотез * исследование и анализ больших неструктурированных объемов информации * формирование материалов аудита Требования * высшее образование (возможно на последнем курсе) – физмат, мехмат, техническое, экономическое * знакомство с подходами искусственного интеллекта, владение навыками Data Scientist/Data Engineer/Data Analyst * готовность разбираться в бизнес-процессах * готовность к самообучению и развитию AI компетенций для применения в работе (Python и GUI/CLI AI инструменты) * знание Python, SQL, желательно PySpark, GreenPlum, PostgreSQL, Hadoop, Hive, Qlik Sense * навыки решения нестандартных задач и выработка инновационных методов их решения * инициативность, умение работать в условиях многозадачности, самостоятельность. Условия * корпоративный спортзал и зоны отдыха * уникальная система обучения Сбера для профессионального и карьерного развития * расширенный ДМС и льготное страхование для семьи * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ЦБ * бесплатная подписка СберПрайм+ * корпоративная пенсионная программа * реферальная программа для сотрудников: можно пригласить в команду знакомых профессионалов и получить вознаграждение.

Location: Москва Workplace: Гибридный О компании Мы ищем стажера в команду продукта DataHouse для решения задач, связанных с аналитическим сопровождением работы процессов экосистемного хранилища данных. Это отличная возможность узнать, как работает современное хранилище данных изнутри, и приобрести опыт работы со стеком Hadoop, Pyspark, Airflow. Обязанности Отслеживать работу регламентных процессов обновления данных, реагировать на сбои и исследовать причины этих сбоев; Выполнять рутинные задачи: постановка на мониторинг, закрытие инцидентов, консультирование коллег по типовым вопросам; Выгружать данные из корпоративных систем и проводить их анализ, готовить отчеты; Подключать новые источники данных к системе мониторинга, настраивать дэшборды и алерты; Писать скрипты и Web-приложения для автоматизации решения аналитических задач, в том числе с использованием LLM; Взаимодействовать с дата-аналитиками, инженерами данных, техлидами, DevOps, Data Governance, сотрудниками поддержки и пользователями данных в рамках текущих задач Требования В том числе Студенты бакалавриата, специалитета или магистратуры; Базовое знание SQL; Опыт работы с данными на Python (например, polars / pandas) на уровне базовых операций (чтение, агрегирование, соединение); Будет плюсом опыт работы с Airflow, Grafana; Готовность приобретать опыт на реальных задачах; Умение ответственно подходить к своей работе и поручениям руководителя; Умение организовывать свою деятельность (соблюдение сроков, формирование базы знаний, оформление документации и т.д.) Условия Cобственную платформу MTS Ocean для получения ИТ-ресурсов, а это значит, что деплой, мониторинг, observability — не будут для тебя проблемой, ты сможешь сосредоточиться на фичах; Профессиональные гильдии инженеров, где мы поддерживаем друг друга и помогаем стать лучше; Внутреннюю площадку TechTalks для обмена опытом, дискуссий, развития навыков самопрезентации; Полезные курсы и вебинары в корпоративном университете и электронную библиотеку. А еще: Прием врачей общей практики и массаж в офисе; Мобильная связь за счет компании и льготные тарифы для близких; Подписка на онлайн-кинотеатр KION, сервис МТС Музыка, книжный сервис Строки от МТС, безлимитные мессенджеры и соцсети.

We are looking for a Middle Data Engineer with strong Snowflake, SQL and dbt skills to build and support production data pipelines. • 2+ years of data engineering experience.

Location: Intellias Meet your recruiter Elena Dimitrova elena.dimitrova@intellias.com https://www.linkedin.com... Vacancy details Data Engineering Data Engineer Senior Bulgaria, Croatia, Poland, Portugal, Spain, Ukraine Remote APPLY NOW REFER A FRIEND We are hiring a Senior Data Engineer to join an Intellias delivery team on a large-scale enterprise data platform migration programme for a financial services client. This is a hands-on senior role. You will write production code every week, own architectural decisions on your workstream, and mentor a paired mid-level engineer. What project we have for you Our client is an independent, active global asset manager with over R3 trillion in assets under management. They are executing a firm-wide data strategy to govern, manage and engineer data as a product across more than 70 business-owned sub-domains. As part of this, they are migrating to Databricks as the foundational layer of their Enterprise Data Platform, adopting a lakehouse architecture built on open formats, declarative pipelines and Unity Catalog. What you will do Perform the end-to-end delivery of one or more priority data domains on Azure Databricks. Design and build medallion (Bronze / Silver / Gold) pipelines using Lakeflow Declarative Pipelines, Auto Loader, Structured Streaming and Delta Lake with Liquid Clustering. Register curated data products in Unity Catalog with the correct tags, masks, row filters, lineage and access policies. Implement data quality gates at the Silver-to-Gold boundary, and refine rules with domain stewards. Package and deploy work using Databricks Asset Bundles through Azure DevOps CI/CD Write automated tests and documentation Work at AI-as-Collaborator level today with a path to AI-as-Orchestrator, using Claude Code, Databricks Assistant, GitHub Copilot and MCP-connected agent tooling to accelerate the recurring parts of pipeline build, test and deploy. What you need for this 6+ years of data engineering experience, with at least the last three years on Databricks in production. Deep hands-on experience with medallion / Lakehouse architecture, Delta Lake, Unity Catalog, Lakeflow Declarative Pipelines and Databricks Asset Bundles. Strong python, PySpark and SQL, comfortable diagnosing performance issues on large workloads through Spark UI and Photon. Real production experience with streaming ingestion (Kafka Structured Streaming, Auto Loader) and CDC patterns. Experience implementing data quality at scale (Great Expectations or equivalent), plus lineage, cataloguing and access control. CI/CD on data platforms through Azure DevOps or GitHub Actions, with infrastructure as code (Terraform or equivalent). Fluent working English. Nice to have Active Databricks certifications Prior exposure to investment management platforms, asset management operations data, market data feeds from major providers CDMP DAMA certification or equivalent data governance credential. Experience applying agentic engineering tooling to production data engineering (not just personal productivity), including MCP-connected Databricks or cloud servers. What it’s like to work at Intellias At Intellias, where technology takes center stage, people always come before processes. By creating a comfortable atmosphere in our team, we empower individuals to unlock their true potential and achieve extraordinary results. That’s why we offer a range of benefits that support your well-being and charge your professional growth. We are committed to fostering equity, diversity, and inclusion as an equal opportunity employer. All applicants will be considered for employment without discrimination based on race, color, religion, age, gender, nationality, disability, sexual orientation, gender identity or expression, veteran status, or any other characteristic protected by applicable law. We welcome and celebrate the uniqueness of every individual. Join Intellias for a career where your perspectives and contributions are vital to our shared success. Skills Databricks

We are looking for a Middle Data Engineer with strong Snowflake, SQL and dbt skills to build and support production data pipelines. • 2+ years of data engineering experience.

Location: Belgrade, Serbia; Cluj-Napoca, Romania; Dnipro, Ukraine; Kharkiv, Ukraine; Krakow, Poland; Kyiv, Ukraine; Larnaca, Cyprus; Lodz, Poland; Lublin, Poland; Lviv, Ukraine; Odesa, Ukraine; Remote.BG, Bulgaria; Remote.PL, Poland; Riga, Latvia; Sofia, Bulgaria; Varna, Bulgaria; Warsaw, Poland; Wroclaw, Poland We are looking for a Strong Middle / Senior Databricks Developer with Azure to build scalable data platforms, develop complex data pipelines, and support cloud based data engineering solutions. The Data Platform project focuses on delivering cloud based data engineering solutions in the Microsoft Azure ecosystem. You will work with Databricks, Spark, and Azure services to build and optimize reliable data pipelines and support modern Lakehouse architecture. Design data solutions using Databricks Develop data pipelines for structured and unstructured data Build ETL and ELT workflows in Azure Develop high performance solutions using PySpark and Spark SQL Design Lakehouse architectures using Delta Lake Integrate data from databases APIs cloud services and streaming platforms Optimize Spark workloads for performance scalability and cost efficiency Ensure data quality governance security and monitoring standards Collaborate with Data Engineers Data Architects Analytics teams and business stakeholders Participate in architectural discussions and technical decision making Support CI and CD processes and infrastructure automation practices 4+ years of experience in Data Engineering 3+ years of hands on experience with Azure Databricks Experience with Apache Spark and PySpark Knowledge of Python and SQL Experience with Azure Data Factory Experience with Azure Data Lake Storage Gen2 Experience with Delta Lake and Lakehouse architecture Experience implementing end to end data pipelines in Azure Understanding of data modeling principles and best practices Experience working with large scale datasets in cloud environments Experience with Git based version control systems Upper Intermediate or higher English level Experience optimizing Spark workloads Experience collaborating with cross functional technical and business teams Experience supporting CI and CD processes Experience with infrastructure automation practices

Проектирование, создание и обслуживание масштабируемых конвейеров данных для получения, интеграции и управления данными из различных источников и систем генерации данных (например, лабораторные системы, MES, производство клинических препаратов, системы качества, внешние партнеры).

В этой роли вы будете исследовать новые подходы к обнаружению мошенничества: работать с большими объёмами транзакционных данных, формулировать и проверять ML-гипотезы, строить прототипы и определять, какие решения имеют потенциал для внедрения.

• Прогнозировать спроса для товаров без истории продаж («новинок»): разрабатывать модели холодного старта на основе атрибутов товара, категории, характеристик продавца и... • Python, PySpark, JupyterHub, SQL, Airflow, Vertica, Kafka, ClickHouse, Grafana, Transformers.

Location: Нижний Новгород Мы – команда экспертов, объединенных общей страстью к искусственному интеллекту и рекомендательным системам (RecSys). Нашей главной задачей является создание современной, масштабируемой рекомендательной платформы, способной предвосхищать ожидания пользователей и предлагать персонализированные рекомендации на каждом этапе их взаимодействия в Сбер. Наши решения охватывают широкий спектр отраслей: от финансов и e-commerce до индустрии развлечений и здравоохранения. Если вам близка идея участия в развитии рекомендательной системы, которая помогает миллионам пользователей, присоединяйтесь к нам! Обязанности * поиск подходящих ML решений для решения бизнес-задач, проведение оффлайн и онлайн (А/Б) экспериментов * разработка и улучшение ML-пайплайнов для задач RecSys: подготовка данных, обучение моделей, валидация и внедрение * анализ и работа с большими объёмами данных на PySpark * исследование и применение актуальных методик в области Data Science, Machine Learning и RecSys * взаимодействие с командой разработки и аналитики, генерация гипотез, участие в обсуждении решений и донесение результатов своей работы до команды. Требования * опыт работы в области Data Science от 1 до 3 лет * интерес к рекомендательным системам, желание развиваться и постепенно брать на себя больше ответственности * практические навыки решения ML-задач: от подготовки данных и выбора алгоритмов до настройки гиперпараметров и оценки качества моделей * хорошая база в алгоритмах машинного обучения и интеллектуальном анализе данных; опыт применения классических ML- и/или DL-подходов будет преимуществом * хорошее знание Python и основных DS-фреймворков (PyTorch, PySpark и др.), понимание принципов написания качественного кода * способность самостоятельно разбираться в новых подходах, читать технические материалы на английском и применять их на практике. **Будет плюсом:** * навыки работы с генеративными AI-моделями * опыт создания AI-агентов и использования их в работе будет преимуществом * опыт использования GigaChat, Kandinsky и аналогов в продуктах, навыки создания и использования AI-агентов * инструментальное владение AI для анализа, генерации и автоматизации. Условия * гибридный/офисный формат работы (опционально). Офис расположен по адресу Нижний Новгород, Октябрьская, 35 лит. А * ежегодный пересмотр зарплаты, квартальная и годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * программа адаптации и помощь руководителя на старте * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера.

Location: Intellias Meet your recruiter Smita Aage smita.aage@intellias.com https://www.linkedin.com... Vacancy details Data Engineering Data Engineer Principal India Hybrid APPLY NOW REFER A FRIEND Our client is a company that enables people, enterprises, and cities to harness the power of location. By making sense of the world through the lens of location, it empowers their customers to achieve better outcomes from helping a city manage its infrastructure or an enterprise optimize its assets to delivering drivers to their destination safely. What project we have for you Our client is a is a location data and technology platform company that empower customers to achieve better outcomes – from helping a city manage its infrastructure or a business optimize its assets to guiding drivers to their destination safely. They create solutions that fuel innovation, provide opportunity and foster inclusion to improve people’s lives. If you are inspired by an open world and driven to create positive change, join us! As a Lead/Principal Engineer in the Map Data Processing group you will develop smart Map Data processing tools for the state-of-the-art Mapping Technologies. You will work self-sustained in an agile team. Your responsibility will cover developing, extending and maintaining tool and services that processes map data for global navigational database. For that you will be translating product strategies into technology strategies, leading the long-term architectural direction and helping design and build industry-grade customer-facing geo-data-intensive products. You will work closely with other engineering & operations teams, internal users of tool chains your team develops and partner with product managers and the larger map operations business units. What you will do Participate in the development, and maintenance of Python applications. Troubleshoot and debug applications to resolve issues. Write clean, efficient, and self-documented code. Implement automation tests. Collaborate with team members to identify and solve technical problems. Participate in code reviews to ensure code quality and maintainability. Work with stakeholders to gather and analyze requirements and provide technical solutions. Monitor application performance and provide optimization recommendations. Stay up to date with the latest technologies and industry trends. What you need for this Join our engineering organization to build the next generation of automated map data processing for a global provider of location and mapping solutions used in automotive and mobility. You will be one of the founding engineers on Platform — a system that takes road data from multiple sources (GPS traces, traffic signs, open map data), detects errors in the map, and either auto-corrects them or flags them for human review. The platform produces quality signals and road attribute corrections that feed directly into navigation and autonomous driving products. This is a project-starter role. We are looking for someone who can define technical direction, shape architecture from scratch, align stakeholders across teams, and raise the engineering bar — not someone who joins and executes tasks. The primary language on this team is Python/Pyspark — strong technical leads in either are welcome. Required skills: 8+ years of experience working as a Python Engineer/ Pyspark into Big Data Eco system or in a similar role. Strong Experience in AWS, Spark, Big Data Eco system Strong proficiency in Python programming and knowledge of object-oriented programming principles. Solid understanding of algorithms and data structures. Experience with OpenSearch / Elastic Search i.e. Understanding how the index’s works and how the layer are stitched together, also how to create schemas. Experience in Prompt Engineering to maintain, enhance or modify the prompts used for AWS Bedrock. Proficiency in ETL, protobuf. Experience with Amazon Web Services (AWS). Experience with API Gateway, Lambda’s, Step-Functions. Proficiency in version control systems, particularly Git. Knowledge of Agile methodologies, such as Scrum, and experience working in Agile teams. Strong problem-solving skills and the ability to debug and optimize code. Excellent communication and teamwork skills, with the ability to collaborate effectively with cross-functional teams. Will be a plus: Experience with HDLive maps, Open Drive, etc. Technologies: Python (primary — one or the other) Big Data, Spark, Pyspark AWS (Step Functions, ECS, Lambda, EMR) CI/CD (GitHub / GitLab) What it’s like to work at Intellias At Intellias, where technology takes center stage, people always come before processes. By creating a comfortable atmosphere in our team, we empower individuals to unlock their true potential and achieve extraordinary results. That’s why we offer a range of benefits that support your well-being and charge your professional growth. We are committed to fostering equity, diversity, and inclusion as an equal opportunity employer. All applicants will be considered for employment without discrimination based on race, color, religion, age, gender, nationality, disability, sexual orientation, gender identity or expression, veteran status, or any other characteristic protected by applicable law. We welcome and celebrate the uniqueness of every individual. Join Intellias for a career where your perspectives and contributions are vital to our shared success. Skills AWS BigData CI/CD Python Spark

Location: Poland; Georgia; Serbia; Bulgaria; Czechia; Kazakhstan We are looking for a Data Engineer to join our team and help build and maintain scalable data pipelines and data warehouse solutions. In this role, you will work with large datasets, design efficient data processing workflows, and ensure the reliability and performance of our data platform. Since 2007, MMDSmart has been a leading supplier in cloud-based business communications, delivering game-changing IT products to businesses in over 100 countries. Our success is fueled by the inspiration and innovative ideas of our team members, encouraging their growth and impacting our Messaging and Voice solutions. These empower our clients to communicate more effectively with their customers. Don’t miss this chance to excel in a rapidly expanding tech company, where your potential and ambitions are not just recognized, but celebrated. Requirements What will you do at MMDSmart? Design, build, and maintain data pipelines for batch and streaming data processing; Develop and optimize ETL workflows using Python and SQL; Build and maintain Airflow DAGs for orchestration of data workflows; Implement data transformations and data quality checks; Optimize BigQuery queries and data models for performance and cost efficiency; Troubleshoot and improve reliability of existing pipelines; Collaborate with analysts and engineering teams to support data needs. You are a perfect match if you have: 4+ years of experience as Data Engineer; Strong experience with SQL and Google BigQuery; Experience building data pipelines and ETL processes; Solid knowledge of Python for data processing; Hands-on experience with Apache Airflow; Experience working with large datasets; Understanding of data modeling and data warehouse concepts; Experience with query optimization and cost-efficient data processing; Experience with data pipeline monitoring and observability; Lannguage knowledge: fluency in Russian and English. Nice to have: Experience with Kafka or streaming data pipelines. You will primarily focus on: Building and maintaining data pipelines; Batch and streaming data processing; Data transformation and orchestration; Performance and cost optimization. Our Tech Stack Google BigQuery; Python; Apache Airflow; Apache Spark (PySpark). The role does not involve DevOps, ML, or BI development. Benefits Why do talents choose and stay at MMDSmart? Embrace the opportunity to work in a company that cares about: Your Wellness: Access to mental health programs, comprehensive medical insurance, and a sports card*. Your Happiness: Enjoy the company of cool teammates, a friendly atmosphere, a strong mentorship, and a variety of bonuses & corporate gifts. Your Development: Benefit from free educational events & courses, and English classes with native speakers. Your Career: Focus on your professional development in a supportive environment. Work-Life Balance: Choose your working format - remote, office-based, or hybrid and engage in team-building activities. Think this role's got your name on it? Go ahead and apply, we'd love to see what you've got! Equal-Opportunity Employment We are an equal opportunity employer committed to creating a diverse and inclusive work environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. *Medical Insurance and Sport Card perks are available for employees located in Bulgaria.