Задачи:
— Разрабатывать и внедрять ML-модели для прогнозирования поведения клиентов-физических лиц и розничного продуктового портфеля Банка
— Рассчитывать ожидаемые денежные потоки по кредитным и пассивным продуктам Банка
Требования:
— Опыт работы в банковской сфере
— Опыт разработки и внедрения ML-моделей от сырых данных до production
— Работа с табличными данными
— Свободное владение Python: Pandas, NumPy, Matplotlib (или аналогичными инструментами визуализации)
— Знание SQL: оконные функции, сложные JOIN, оптимизация запросов к БД
— Хорошее владение ML-библиотеками: CatBoost, LightGBM, XGBoost, Scikit-learn
— Умение проводить стресс-тесты и А/В-тесты
— Понимание особенностей розничных банковских бизнес-процессов
— Преимуществом будет знание MLflow, Airflow, Butch Scoring, Docker, Spark / PySpark
Условия:
— Офисный формат работы
— Офис в центре Москвы
Мы ищем: Талантливого ML-разработчика с временными рядами и классикой, в области прогнозирования в ритейле на стажировку, которому предстоит работать над интересными высоконагруженными проектами.
Требования:
— Опыт в Data Science / Machine Learning от 2 лет;
— Прагматизм: готовность искать рабочие решения, а не идеальные, но нереализуемые модели;
— Глубокое знание Python, SQL и PySpark;
— Разработка ETL-процессов для обработки больших данных (чтение, трансформация, агрегация, запись);
— Оптимизация Spark-запросов (партиционирование, кэширование, работа с broadcast-переменными);
— Знание основных ML-фреймворков (Scikit-learn, TensorFlow/PyTorch);
— Опыт работы с данными: обработка, анализ, feature engineering.
Будет плюсом:
— Опыт продакшн-разработки (не только исследования);
— Понимание, как устроены процессы в бизнесе, а не только в Jupyter Notebook;
— Умение запускать ML-модели в PySpark;
— Использование Spark MLlib для распределенного обучения;
— Работа с PySpark Pandas UDFs для эффективного применения ML-моделей к большим данным.
Условия:
— Работа в команде экспертов;
— Широкий технологический стек, сотни проектов;
— Гибкий график работы, который позволяет высыпаться и выделять время на хобби;
— Возможность разрабатывать IT-решения для разных отраслей, пробовать новые технологии;
— Процессы удаленной работы и просторные офисы с зонами коворкинга;
— Возможность прокачаться во всех интересующих направлениях: стать тимлидом, архитектором, разработчиком full-stack;
— Развитая система наставничества, сертификация за счёт компании, участие в конференциях, изучение английского языка;
— Активный обмен опытом, внутренние и внешние митапы, хакатоны, доклады по развитию hard и soft skills;
— 50+ клубов по интересам и профессиональных сообществ внутри компании;
— Корпоративная база знаний и социальная сеть для удобства общения с каждым из 1500+ сотрудников.
Дополнительно:
— Набор осуществляется по ИТ специальностям, указанным в Приказе Минцифры № 27 от 22.01.2026г.
— Решение Министерства цифрового развития, связи и массовых коммуникаций РФ о предоставлении государственной аккредитации организации от 10.03.2022 №АО-20220309-3771014210-3
УдалённоСтажёрБудет плюсом: Опыт продакшн-разработки (не только исследования); Понимание, как устроены процессы в бизнесе, а не только в Jupyter Notebook; Умение запускать ML-модели в PySpark; Использование Spark MLlib для распределенного обучения; Работа с PySpark Pandas UDFs для эффективного применения ML-моделей к большим данным
Задачи:
— участвовать в полном цикле разработки моделей по рискам розничного бизнеса: постановка задачи, сбор данных, разработка ML-модели, подготовка артефактов для внедрения
— исследовать источники данных и современные архитектуры для повышения качества работы моделей
— написание алгоритмов для целей сегментации выдач по риск-доходности
— презентация результатов исследований бизнес-заказчикам.
Требования:
— опыт разработки моделей машинного обучения (классический стек)
— знание ключевых методов и алгоритмов в области Machine Learning
— знание Python и библиотек Pandas, Scikit-learn, XGBoost/LightGBM/CatBoost, Transformers
— навыки работы с базами данных, знание SQL, PySpark
— высокий уровень ответственности и самостоятельности
Будет плюсом:
— опыт стажировок
— опыт с LLM и AI-агентами
— учебные знания в области розничного кредитования, портфельного управления и математики кредитных рисков.
Условия:
— оплачиваемая стажировка
— возможность выбрать удобный график – офис или гибрид (локация: м. Кутузовская)
— корпоративный спортзал и зоны отдых.
Задачи:
— сопровождение и развитие аналитической отчетности с фокусом на работу с данными, SQL-логикой и корректностью бизнес-показателей;
— автоматизированный отчет, который обращается к данным витрины с использованием PySpark, обработка данных на Python и Pandas, формирование итоговой HTML-страницы;
— анализ и доработка источников данных;
— контроль корректности показателей, актуальности бизнес-логики, качества данных и стабильности формирования отчета;
— доработка SQL-логики, обработки данных, структуры отчета и итоговой HTML-страницы.
Дополнительно:
— участие в развитии AI-агента по отчетности, автоматизирующего text2sql-задачи: перевод пользовательских запросов в SQL, сбор данных, проверка показателей, подготовка структурированных ответов для руководства банка;
— понимание SQL, аналитического контекста, структуры витрин данных;
— проверка корректности сгенерированных запросов и результатов;
— участие в настройке логики взаимодействия агента с данными.
Требования:
— опыт работы в разработке на Python от 3 лет;
— уверенное владение Python, Pandas, SQL, PySpark (Hadoop/Spark);
— знание matplotlib/seaborn, Jupyter Notebook;
— знание HTML;
— навыки работы с Git, умение читать код DS и приводить его к инженерным стандартам production.
Условия:
— график работы 5/2, офисный формат (м. Кутузовская);
— оформление по ТК РФ, оплачиваемые больничные листы;
— спортивные и корпоративные мероприятия;
— программа обучения;
— добровольное медицинское страхование;
— и многое другое.
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.
Location: Москва
Команда SberAds занимается созданием предиктивных моделей и высоконагруженных сервисов для показа рекламы в интернете. Наши модели участвуют в реальных аукционах, влияя на то, какая реклама отображается пользователю. Это позволяет нам улучшать качество рекламы, повышать удовлетворённость рекламодателей и увеличивать эффективность платформы.
Обязанности
Чем предстоит заниматься:
* создавать и совершенствовать модели для различных этапов процесса показа рекламы
* выполнять полный цикл DS/ML-задач
* развивать компетенции DS
* выступать заказчиками удобной и быстрой платформы для работы DS/ML-инженеров.
Примеры задач:
* разработка эмбеддинг-моделей на основе описаний рекламных объявлений
* внедрение новых признаков в модели прогнозирования конверсий
* обучение моделей для предсказания ставок в аукционах
* проведение экспериментов с оффлайн- и онлайн-признаками в моделях
* доставка признаков до сервисов на Go (при необходимости помощь коллег).
Требования
Мы ждём что ты:
* имеешь глубокие знания классических методов машинного обучения и глубокого обучения
* обладаешь опытом применения промышленных решений RecSys
* способен самостоятельно разрабатывать и сопровождать модели от постановки задачи до эксплуатации
* уверенно владеешь Python и PySpark, имеешь опыт промышленной разработки кода
* понимаешь принципы распределённой обработки данных
* можешь принимать обоснованные решения на основе анализа данных и чётко аргументировать свои выводы
* умеешь решать сложные задачи, разбивая их на подзадачи и контролируя выполнение.
Будет плюсом:
* наличие опыта в задачах ранжирования, поиска или рекламы
* владение языком программирования Go.
Условия
Что мы предлагаем:
* удобный современный офис в Москве (Оружейный переулок, дом 41),
* возможность работать по гибридному графику
* оборудованный корпоративный спортзал и зоны отдыха
* доступ к более чем 400 образовательным программам СберУниверситета для личного и карьерного роста
* расширенную программу добровольного медицинского страхования, льготы на страховки для членов семьи и участие в корпоративной пенсионной программе
* скидку на ипотеку размером в треть ключевой ставки Центробанка России
* подписка Прайм с возможностью совместного использования на трёх близких
* бонус за рекомендации друзей для присоединения к команде Сбера.
Location: Remote (USA)
At Livefront, we help companies design and build world-class digital products that command attention and inspire joy. We’ve helped household names like CVS, Samsung, General Mills, and Optum create experiences that have reached millions of people, and startups like HomeSpotter and Credly build entirely new businesses that challenge their industries’ status quo.
We're looking for an outstanding Senior Databricks Data Engineer to join our growing data practice — someone who builds the data and AI foundations that digital products and intelligent experiences run on.
Who you are
You are a Databricks-focused Data Engineer who understands that great data platforms are only as valuable as the products, AI workflows, and experiences they enable. You bring deep, production-grade expertise across the Databricks platform and know how to connect platform capabilities to real business outcomes.
You thrive in ambiguity and can quickly assess a client's data landscape to recommend and implement the right solutions. You understand that in consulting, your Databricks depth is most valuable when it connects platform capabilities to the products and experiences clients actually use — and you're as comfortable in a product design conversation as you are building a DLT pipeline.
You excel at translating complex data challenges into clear technical requirements and can confidently navigate conversations with everyone from data scientists to executives. Your engineering principles are mature and grounded in real-world experience across various industries and scales.
You have an interest in and a curiosity about data platforms and the latest advances in data technology.
What you will be doing
Design and build production data pipelines using Lakeflow Declarative Pipelines, Autoloader, and Structured Streaming, with end-to-end ownership of ingestion, transformation, data quality expectations, and CI/CD deployment via Declarative Automation Bundles.
Architect and implement Lakehouse solutions on Databricks — medallion architecture, Delta Lake, Unity Catalog — tailored to the client's analytics, AI, and application needs.
Build and maintain Databricks transformation layers — DLT pipelines, PySpark notebooks, and dbt — with data quality constraints and SLAs baked in.
Design and maintain the data and AI foundations — Unity Catalog, Feature Store, MLflow, and Model Serving — that power production ML, agent workflows, and AI-enabled digital products.
Collaborate with product and backend engineers to design data models, APIs, and application data contracts — ensuring the platform serves the product, not just the warehouse.
Consult with clients to understand their data challenges, develop data strategies, and implement sustainable solutions.
Adapt your approach based on project needs — sometimes leading data architecture discussions with clients, other times supporting internal teams with specialized data expertise.
Work within multi-cloud environments — primarily AWS and Azure — anchoring data platform recommendations around Databricks where it fits the client's architecture and goals.
Champion data governance through Unity Catalog — access control, lineage, data quality policies, and compliance — as a first-class part of every engagement, not an afterthought.
Design data-to-application architectures — including Lakebase-backed services and Databricks Apps — that connect governed data to AI workflows, digital products, and user-facing experiences.
Help build Livefront's Databricks practice — contributing to accelerators, internal enablement, certification goals, and Databricks partner go-to-market materials alongside delivery work.
Why you should apply
You want to work with passionate and talented people who are always looking for ways to make things better.
You desire a work environment where respect, mutual trust, and egoless collaboration are paramount.
You want colleagues who take their work seriously but not themselves, and who know how to let loose and have a good time.
You like being part of a team with a reputation for excellence that gives back to the community by educating, mentoring, and sponsoring.
You want to work on products and accounts that have outsized impact and reach.
You believe in sweating the details, giving a damn about quality, and taking pride in going the extra mile.
You want to help build a data practice specialization from the ground up — shaping how we go to market with Databricks, what we build as accelerators, and what it means to do this kind of work at a digital product company.
What you bring to the table
7-10 years of data engineering experience with at least 5 years in production Databricks environments, preferably in a consulting or client delivery context.
Solid working knowledge of AWS and Azure cloud services relevant to Databricks deployments — storage, networking, IAM, and compute — with GCP familiarity a plus.
Deep, production-grade Databricks expertise: Lakeflow Declarative Pipelines, Autoloader, Structured Streaming, Lakeflow Jobs, Unity Catalog (including fine-grained access control and lineage) — demonstrated through shipped production workloads, not prototypes.
Proven experience designing Lakehouse architectures — medallion patterns, Delta Lake table design, partitioning, Z-ordering, and query optimization — at production scale.
Hands-on experience with data pipeline testing, observability, and CI/CD for data — including unit testing, data quality frameworks, and version-controlled deployments via Git and Declarative Automation Bundles.
Strong proficiency in SQL and Python, with the ability to write clean, performant, and maintainable code.
Understanding of data modeling, schema design, and query optimization.
Excellent communication skills with the ability to explain complex data concepts to both technical and non-technical stakeholders.
Strong problem-solving skills with the ability to navigate ambiguous requirements and deliver pragmatic solutions.
Above-average discipline and personal organization skills.
Obvious comfort with critique and peer review in the context of an iterative development process.
A demonstrated hunger for personal and professional growth.
A self-evident love and care for the craft of data engineering.
Bonus points if you…
Have worked with real-time streaming technologies (Kafka, Kinesis, etc.).
Have hands-on experience with alternative cloud data platforms — useful context for migrations and competitive assessments, though Databricks is our primary platform focus.
Have experience in healthcare or fintech domains.
Have hands-on experience with MLOps or LLMOps on Databricks — MLflow experiment tracking, model registry, Model Serving endpoints, or Vector Search for RAG pipelines.
Have experience with Java, Go, or Scala.
Have strong illustration skills for technical diagramming and data architecture documentation.
Speak, write, and/or educate publicly about data engineering topics.
Have contributed to open-source data projects.
Hold or are actively pursuing a Databricks certification (Data Engineer Associate or Professional, or Apache Spark Developer) — we treat these as meaningful signals of platform depth, and they directly support our Databricks partner growth goals.
Have experience with Databricks Apps, or Lakebase — early familiarity with where the Databricks platform is heading is a strong differentiator.
What to expect
When applying, please include a short note about yourself, a summary of your work experience, and a link to any public profiles you actively maintain (e.g., GitHub, LinkedIn, etc).
Our hiring process moves quickly and consists of several stages for candidates who capture our attention with their initial submission, sometimes including but not limited to a short preliminary phone interview, a series of video interviews, and a short take-home exercise, which you'll have up to a week to complete.
Compensation is $150,000 - $180,000
Additional information
We go out of our way to evaluate all employees and job applicants equally based on merit, competence, and qualifications. We encourage candidates from all backgrounds to apply and consider all qualified applicants. Don't worry, every application will be reviewed by a human.
Location: Warszawa, Poland; Almaty, Kazakhstan; Prague, Czechia; Tallinn, Estonia; Tbilisi, Georgia; Budapest, Hungary; Rīga, Latvia; Vilnius, Lithuania; Lisboa, Portugal; Bucharest, Romania; Madrid, Spain
Workplace: fully
Customer Description:
The customer is a global mobility and urban services platform providing transportation and other on-demand services through a digital marketplace.
Project Description:
The work spans deep learning model development, large-scale geospatial data pipelines, and low-latency production serving. The candidate will measure the impact through offline evaluation metrics and the results of online experiments
Project Phase:
ongoing
Soft Skills:
• Ability to influence teammates and cross-functional stakeholders effectively.
• Curious and improvement-oriented mindset with a willingness to challenge existing approaches.
• Excellent ability to communicate complex technical findings in a clear and concise manner.
Hard Skills / Must Have:
• 5+ years of machine learning engineering experience building and deploying deep learning models in production.
• Experience building regression, forecasting, or other supervised machine learning systems for production prediction tasks.
• Expert-level proficiency in Python and its core data science libraries (e.g., PySpark, Pandas, NumPy, Scikit-learn, PyTorch; gradient-boosting libraries such as CatBoost/XGBoost/LightGBM).
• SQL.
• Ability to design an ML system from scratch, including data analysis and processing.
• Experience translating business goals into ML problems with appropriate metrics and non-functional requirements.
• Experience designing and evaluating ML experiments.
• Experience with MLOps tools.
• Experience working with large-scale geospatial and behavioral datasets.
• Experience deploying models to production on ML serving infrastructure and optimizing for latency, and awareness of concept drift and how to detect and manage it.
• Comfort working with large-scale geospatial and behavioral data (e.g., GPS traces, H3 spatial indexing)
Hard Skills / Nice to Have (Optional):
• Academic background in Computer Science, Mathematics, or a related discipline.
• Experience with travel time prediction, traffic estimation, or routing quality.
• Experience with open-source routing engines.
• Knowledge of map matching, speed profiles, road graph tiles, and historical traffic.
• Experience with mapping, location, or geospatial products.
• Experience building products for developing markets.
• Experience with cloud data and machine learning platforms.
Responsibilities and Tasks:
• Design and build machine learning models to improve routing and travel time prediction.
• Develop traffic estimation models using large-scale GPS data.
• Implement map-matching solutions for noisy GPS data.
• Improve travel time calculation, smoothing, and rerouting logic.
• Translate routing objectives into machine learning objectives and evaluation metrics.
• Lead offline and online model evaluation activities.
• Collaborate with backend engineers to deploy low-latency production models.
• Partner with product and operations teams to define new features and requirements.
• Own the production ML lifecycle, including serving, monitoring, drift detection, and retraining pipelines.
🧪
Technology Stack:
}Python, SQL, PySpark, Pandas, NumPy, Scikit-learn, PyTorch, XGBoost, LightGBM, CatBoost, MLOps, ML Lifecycle Management, Production ML Systems, ML Infrastructure, Geospatial Analytics, GPS Data, H3 Spatial Indexing
📝 Additional notes:
The work will be carried out either on client's equipment or via a virtual workstation. Requires
fluent Russian
, as part of the team communication is conducted in Russian
Обязанности:
• разработка и совершенствование End-to-End ML-пайплайнов
• разработка продакшен-пайплайнов обработки данных
• работа с огромными объёмами данных Сбера (петабайты) на PySpark, исследование подходов применения их в моделях
• писать эффективный и масштабируемый код для тренировки и инференса моделей на PyTorch, проводить эксперименты на GPU-кластере
• performance оптимизации кода по обработке больших массивов данных или онлайн сервисов рекомендаций с высокой нагрузкой
• менторинг младших членов команды, обмен знаниями и экспертизой.
Требования:
• математический бэкграунд
• хорошее знание Python и ключевых фреймворков для работы с данными (PySpark, PyArrow, Pandas)
• опыт написания качественного production кода
• опыт написания промышленных пайплайнов обработки данных, содержащих множество шагов, зависимостей и сложную логику
• опыт использования Airflow (или другого industry-standard оркестраторов пайплайнов, т.к. Luigi, Dagster и т.д.)
• хорошее понимание баз данных SQL / NoSQL
• 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.
Условия:
• комфортный современный офис рядом с м. Кутузовская (опционально)
• возможность выбрать удобный график – офис/гибрид/удаленка (опционально)
• ежегодный пересмотр зарплаты, квартальная и годовая премия (премия указывается в зависимости от должности и системы премирования)
• корпоративный спортзал и зоны отдыха
• более 400 образовательных программ СберУниверситета для профессионального и карьерного развития
• программа адаптации и помощь руководителя на старте (для вакансий уровня Junior)
• 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.
Задачи:
— Решать сложности бизнес-заказчиков и оценивать полученный результат.
— Планировать работу команды (Data Scientists, ML-engineers).
— Проектировать развитие системы прогнозирования спроса.
— Проектировать с руководителями смежных технических команд совместные интеграции.
— Программировать и проводить code review.
Требования:
— Опыт работы в ML и DS проектах от 5 лет.
— Опыт решения задач прогнозирования временных рядов.
— Управленческий опыт работы.
— Умение писать на Python.
— Умение работать с данными на PySpark, SQL или Pandas.
— Знание и понимание классических ML алгоритмов (линейная регрессия, деревья решений и градиентный бустинг).
— Знание Deep Learning фреймворков (Pytorch, Transformers).
Location: Kazakhstan; Kyrgyzstan; Uzbekistan
Workplace: Hybrid
Salmon is a technology-driven financial company building a banking and lending platform across Southeast Asia, starting in the Philippines.
We combine global fintech expertise with deep local market knowledge to make financial services simple, accessible, and useful for millions of people across the region.
7M+ app downloads. 2M+ monthly active users. 7,000+ partner stores. US$310M+ raised from leading global investors.
Manila-based, globally distributed, and hybrid-first — our team spans 45+ countries.
If you want to solve complex problems at scale and impact how millions of people access and manage money, come build with us.
Southeast Asia's fintech moment starts here.
About the role
You'll build and own the data pipelines and lakehouse architecture that turn raw data into reliable inputs powering products used by millions of people.
What you'll do
- Design and run batch and streaming pipelines on Databricks and Apache Spark over AWS, ingesting from databases, APIs, and event streams.
- Shape our lakehouse architecture on S3 and Delta Lake, including medallion (Bronze / Silver / Gold) layering and storage optimization.
- Work directly with analytics, engineering, and product teams to turn raw data into reliable, usable inputs for the business.
What you'll own
- Own projects end to end — from stakeholder alignment and design review through implementation, launch, and ongoing support.
- Build self-service data solutions that let other teams deliver their own data needs, designed to scale and extend easily as new sources and use cases are added.
- Build and maintain scalable ETL/ELT pipelines that are fault-tolerant and cost-optimized.
- Optimize Spark jobs end to end — cluster sizing, caching, joins, shuffles, partitioning, and storage formats (Parquet, Delta).
- Manage Databricks Workflows, jobs, cluster configurations, and the underlying infrastructure — security policies, resources, and access — responding to failures and performance issues.
- Implement data governance and access control with Unity Catalog across accounts and environments, including handling of PII.
What makes you a strong fit
- Hands-on experience with Databricks and strong proficiency in Apache Spark (PySpark and/or Scala).
- Strong Python for data engineering, plus SQL and Spark SQL for transformation and analytics.
- Experience with Delta Lake and lakehouse architectures, including performance optimization.
- Proven ownership — you've run projects where you knew the weak points, gathered feedback, and made your solutions easy to scale and maintain.
- Experience writing clean, testable, maintainable code with Git-based version control.
- Experience with infrastructure-as-code such as Terraform or Ansible is a plus — not required, and easy to pick up on the job.
What we offer
Build what's next in Southeast Asian fintech.
Ownership and flexibility
- Stock option plan — four-year vesting with a 25% cliff after the first year
- Hybrid-first, with fully remote work available for eligible roles and core collaboration hours from 12:00 to 19:00 Manila time (UTC+8)
- Company-provided tools and equipment
- Office in Bonifacio Global City, Manila — a modern, walkable district at the centre of the Philippines' growing tech scene
Health and time off
- Medical insurance support for you and your family through co-funding or reimbursement, depending on your location and subject to policy limits
- Access to an internal mental health support specialist
- 22 vacation days, Philippine public holidays, and 15 sick days
- 105 days of paid maternity leave and up to 14 days of paid paternity leave for employees relocating to Manila
Relocation and family support
- Relocation support for you, your family — including flights, two weeks of accommodation, visas, paperwork, and logistics
- Reimbursement of eligible pet relocation expenses
- US$2,500 per year for flights home
- Baby and family stipends for employees relocating to Manila: US$1,000 per month for each child under two and US$500 per month for each child aged two to 18
Growth and team experience
- Opportunities to learn and share your expertise through internal expert meetups, external conferences, speaking opportunities, and industry publications
- Company-sponsored trips to Manila to meet and work with your team in person
- Regular team offsites, sports activities, and team events
- High-performing teams can earn a dedicated beach house week in Southeast Asia
Considering a move to Manila? See what life with Salmon looks like https://www.youtube.com/watch?v=nYQLKhuZHIE&t=34s
Location: Cairo, Egypt; Alexandria, Egypt
Workplace: hybrid
Yassir is the leading super App in the Maghreb region set to changing the way daily services are provided. It currently operates in 45 cities across Algeria, Morocco and Tunisia with recent expansions into France, Canada and Sub-Saharan Africa. It is backed (~$200M in funding) by VCs from Silicon Valley, Europe and other parts of the world.
We offer on-demand services such as ride-hailing and last-mile delivery. Building on this infrastructure, we are now introducing financial services to help our users pay, save and borrow digitally.
Helping usher the continent into a digital economy era. We’re not just about serving people - we’re about creating a marketplace to bring people what they need while infusing social values.
Responsibilities
Build a centralized data lake on GCP data services by integrating diverse data sources throughout the enterprise.
Develop, maintain, and optimize Spark-powered batch and streaming data processing pipelines. Leverage GCP data services for complex data engineering tasks and ensure smooth integration with other platform components.
Design and implement data validation and quality checks to ensure accuracy, completeness and consistency across our data workflows.
Work closely with the AI / ML team to enable AI use cases and in particular address data needs for Machine Learning model development and monitoring.
Collaborate with cross-functional teams, including Data Analysts and other business users from Operations, Marketing and Commercial departments, to extract valuable insights and enable data-driven decision making.
Collaborate with the Product teams to design, implement, and maintain the data models for analytical use cases.
Engage in technology exploration and research, the development of PoC’s, conducting deep investigations.
Design and manage ETL/ELT processes, ensuring data integrity, availability, and performance.
Troubleshoot data issues and conduct root cause analysis when reporting data is in question.
Work on Gen AI related initiatives aligned with the company goals.
Required Technical Skills
PySpark - Batch and Streaming
GCP - Dataproc, Dataflow, DataStream, Dataplex, Pub/Sub, BigQuery and Cloud Storage
NoSQL (preferably MongoDB)
Programming languages: Scala/Python
Great Expectation, or similar DQ framework
Familiarity with workflow management tools like: Airflow, Prefect or Luigi
Understanding of Data Governance, Data Warehousing and Data Modelling
Good SQL knowledge
Business
Able to communicate effectively, distill technical knowledge into digestible messages in a succinct / visual way
Proactively identify and contribute with team development initiatives, and supporting junior members
Good to have skills
Infrastructure-as-Code, preferably Terraform
Docker and Kubernetes
Looker
AI / ML engineering knowledge
Lineage, or relevant tools e.g. Atlan
DBT
At Yassir, we believe in the power of diversity and the importance of an inclusive culture. So, if you're ready to bring your unique perspective and experiences to the table, then we're excited to listen.
Don't just apply for a job, come and be a part of our journey. Let's create a better tomorrow together.
We look forward to receiving your application!
Best of luck,
Your Yassir TA Team
Location: Tunis, Tunisia; Sousse; Tunisia
Workplace: hybrid
Yassir is the leading super App in the Maghreb region set to changing the way daily services are provided. It currently operates in 45 cities across Algeria, Morocco and Tunisia with recent expansions into France, Canada and Sub-Saharan Africa. It is backed (~$200M in funding) by VCs from Silicon Valley, Europe and other parts of the world.
We offer on-demand services such as ride-hailing and last-mile delivery. Building on this infrastructure, we are now introducing financial services to help our users pay, save and borrow digitally.
Helping usher the continent into a digital economy era. We’re not just about serving people - we’re about creating a marketplace to bring people what they need while infusing social values.
Responsibilities
Build a centralized data lake on GCP data services by integrating diverse data sources throughout the enterprise.
Develop, maintain, and optimize Spark-powered batch and streaming data processing pipelines. Leverage GCP data services for complex data engineering tasks and ensure smooth integration with other platform components.
Design and implement data validation and quality checks to ensure accuracy, completeness and consistency across our data workflows.
Work closely with the AI / ML team to enable AI use cases and in particular address data needs for Machine Learning model development and monitoring.
Collaborate with cross-functional teams, including Data Analysts and other business users from Operations, Marketing and Commercial departments, to extract valuable insights and enable data-driven decision making.
Collaborate with the Product teams to design, implement, and maintain the data models for analytical use cases.
Engage in technology exploration and research, the development of PoC’s, conducting deep investigations.
Design and manage ETL/ELT processes, ensuring data integrity, availability, and performance.
Troubleshoot data issues and conduct root cause analysis when reporting data is in question.
Work on Gen AI related initiatives aligned with the company goals.
Required Technical Skills
PySpark - Batch and Streaming
GCP - Dataproc, Dataflow, DataStream, Dataplex, Pub/Sub, BigQuery and Cloud Storage
NoSQL (preferably MongoDB)
Programming languages: Scala/Python
Great Expectation, or similar DQ framework
Familiarity with workflow management tools like: Airflow, Prefect or Luigi
Understanding of Data Governance, Data Warehousing and Data Modelling
Good SQL knowledge
Business
Able to communicate effectively, distill technical knowledge into digestible messages in a succinct / visual way
Proactively identify and contribute with team development initiatives, and supporting junior members
Good to have skills
Infrastructure-as-Code, preferably Terraform
Docker and Kubernetes
Looker
AI / ML engineering knowledge
Lineage, or relevant tools e.g. Atlan
DBT
At Yassir, we believe in the power of diversity and the importance of an inclusive culture. So, if you're ready to bring your unique perspective and experiences to the table, then we're excited to listen.
Don't just apply for a job, come and be a part of our journey. Let's create a better tomorrow together.
We look forward to receiving your application!
Best of luck,
Your Yassir TA Team
Location: Budapest
The Wow
This role sits at the heart of our real-time data platform, empowering teams to build, deploy, and scale data products that drive critical business decisions and customer experiences.
You will design, develop, and operate the core platform capabilities that power streaming data pipelines, event-driven applications, and self-service data products. Working at the intersection of software engineering and data engineering, you will create scalable frameworks, tooling, and developer experiences that enable our teams to deliver reliable solutions with speed and confidence.
A taster of what you will be involved with:
Design and build scalable, reusable frameworks and services
for data ingestion, transformation, and processing.
Develop and maintain the platform's core capabilities, such as orchestration, data quality validation, and schema management tools.
Evolve the platform's architecture to incorporate new technologies that improve scalability, reliability, and cost-effectiveness.
Collaborate with Data DevOps Engineers to ensure platform features are automated, observable, and deployed via CI/CD.
Act as a key contributor to the platform's roadmap, identifying and prioritizing features that unlock the most value for its users.
What we are looking for:
Must haves:
University Degree in Computer Science, Engineering or equivalent.
At least 3 years of hands-on experience building and operating real-time or event-driven data platforms and streaming applications.
Strong software engineering fundamentals, with experience in a language like Python, Scala or Java.
Experience building reusable frameworks, libraries, or internal tools.
Deep knowledge of SQL
and data integration patterns. Experience in modern cloud database technologies such as Snowflake or Redshift are considered an asset.
Experience with data streaming technologies such as Kafka is a must.
Strong experience building distributed data processing workloads using Apache Spark (PySpark and/or Scala).
Hands-on experience designing and operating event-driven architectures on AWS, including services such as MSK, Kinesis Data Streams, EKS, EMR, Lambda, and EventBridge.
Experience in NoSQL databases such as HBase, Cassandra, and DynamoDB.
Excellent verbal and writing English communication skills.
Nice to have:
Familiarity with real time reporting tools like Grafana or Datadog
Familiarity with Data Modelling Concepts like Inmon, Kimball and Data Vault
Knowledge AWS services like Glue, Lambda, Step Functions and S3
Experience with orchestrating data pipelines using tools like Airflow.
Experience building or managing APIs and microservices.
Familiarity with infrastructure scripting like Terraform
and CI/CD practices.
What we offer
Fitness-wellness allowance
🧗♂️
Company mobile phone for private use with 100 GB
📱
Annual HUF devaluation compensation
💡
Hybrid model: 3 days in the office 🏢
& 2 days from home
🏠
Private Health Insurance
🩺
Career development
📈
Technical and soft-skill training opportunities 🎓
Breakfast, fruits & lunch 🍎
Team building events
🥳
Challenge accepted?
By submitting your application, you understand that your personal data will be processed as set out in our
Privacy Policy
Location: Malta
The Wow
This role sits at the heart of our real-time data platform, empowering teams to build, deploy, and scale data products that drive critical business decisions and customer experiences.
You will design, develop, and operate the core platform capabilities that power streaming data pipelines, event-driven applications, and self-service data products. Working at the intersection of software engineering and data engineering, you will create scalable frameworks, tooling, and developer experiences that enable our teams to deliver reliable solutions with speed and confidence.
A taster of what you will be involved with:
Design and build scalable, reusable frameworks and services
for data ingestion, transformation, and processing.
Develop and maintain the platform's core capabilities, such as orchestration, data quality validation, and schema management tools.
Evolve the platform's architecture to incorporate new technologies that improve scalability, reliability, and cost-effectiveness.
Collaborate with Data DevOps Engineers to ensure platform features are automated, observable, and deployed via CI/CD.
Act as a key contributor to the platform's roadmap, identifying and prioritizing features that unlock the most value for its users.
What we are looking for:
Must haves:
University Degree in Computer Science, Engineering or equivalent.
At least 3 years of hands-on experience building and operating real-time or event-driven data platforms and streaming applications.
Strong software engineering fundamentals, with experience in a language like Python, Scala or Java.
Experience building reusable frameworks, libraries, or internal tools.
Deep knowledge of SQL
and data integration patterns. Experience in modern cloud database technologies such as Snowflake or Redshift are considered an asset.
Experience with data streaming technologies such as Kafka is a must.
Strong experience building distributed data processing workloads using Apache Spark (PySpark and/or Scala).
Hands-on experience designing and operating event-driven architectures on AWS, including services such as MSK, Kinesis Data Streams, EKS, EMR, Lambda, and EventBridge.
Experience in NoSQL databases such as HBase, Cassandra, and DynamoDB.
Excellent verbal and writing English communication skills.
Nice to have:
Familiarity with real time reporting tools like Grafana or Datadog
Familiarity with Data Modelling Concepts like Inmon, Kimball and Data Vault
Knowledge AWS services like Glue, Lambda, Step Functions and S3
Experience with orchestrating data pipelines using tools like Airflow.
Experience building or managing APIs and microservices.
Familiarity with infrastructure scripting like Terraform
and CI/CD practices.
What we offer
Much like riding a rollercoaster, sometimes life at Betsson can be lightning fast with twists and turns but always FUN! Then again, what else would you expect from a business 75% millennial and 1700 strong, spread across 7 offices with 900 based out of our Malta HQ alone! We recognise it may not be for the faint-hearted, but if you’re a go-getter, initiator and adrenaline junkie, always striving to push the boundaries and challenge yourself, then you’ll fit right in.
Challenge accepted?
By submitting your application, you understand that your personal data will be processed as set out in our
Privacy Policy
Задачи:
- Реализация высоконагруженных конвейеров обработки данных для обеспечения надежной и бесперебойной репликации данных из ИТ-систем Банка.
- Реализация комплексных задач по подготовке данных в целевых аналитических хранилищах (DataLake, SandBox, FeatureStore) для построения признаков, необходимых для разработки моделей машинного обучения.
- Разработка и поддержание в актуальном состоянии документации по разработанному функционалу.
- Своевременное отражение статуса выполнения задач в Jira.
- Проверка качества кода (код-ревью), написанного инженерами и младшими инженерами данных.
Требования:
- Python - уверенное знание структур данных и алгоритмов, эффективное применение принципов ООП и ФП (Функциональное программирование), опыт написания модульных и интеграционных тестов, знание и опыт применения библиотек обработки и анализа данных - numpy, pandas.
- Опыт разработки и внедрения в промышленную эксплуатацию сервисов загрузки и обработки неструктурированных и слабо структурированных данных (текст, xml, json) из внешних источников.
- Способность разобраться с API поставщиков данных, используя доступную документацию.
- SQL - умение создавать сложные запросы с использованием аналитических оконных функций и использовать инструменты профилирования для оптимизации их производительности, опыт работы с БД Oracle, Postgres, Greenplum.
- Уверенное знание и опыт работы с инструментами разработки, планирования и мониторинга рабочих процессов (workflow engines) пакетной обработки данных.
- Airflow.
- Опыт разработки сложных, высоконагруженных приложений обработки данных на основе PySpark, уверенное знание настроек Spark и их влияния на производительность приложений Spark.
Location: Lisbon, Portugal
JoomPulse
is an AI analytics tool for Mercado Livre and Shopee sellers, providing insights into the most profitable products and fastest-growing categories — helping sellers boost their profits with data-driven decisions.
JoomPulse is one of the key businesses of Joom Group, an international tech-centric group of e-commerce companies that was founded in 2016 in Latvia. Joom Group also includes the following businesses: Joom, a global cross-border marketplace; JoomPro, the first and only end-to-end cross-border B2B marketplace; and Onfy, a pharmaceutical marketplace in Germany. Joom Group has offices in Latvia, China, Germany, and Brazil, with its headquarters in Lisbon, Portugal. We work as one international team, sharing knowledge and collaborating across countries, businesses and products.
Currently, we are looking for an
Senior Product Analyst.
The JoomPulse Product Analyst owns the three things that decide whether the product grows:
The quality of the data on the platform
The product analytics that tell us whether new features and new data actually move conversion and retention
The market analysis that turns our marketplace data into insight for large corporate data clients and for the press
This person makes sure we can trust our own data, makes sure every significant feature and data release is measured against its real effect on conversion and retention, and packages our marketplace data into external-facing market analysis (category trends, pricing, market dynamics) that lands with corporate clients and journalists.
Responsibilities
Transform imperfect data into clear, actionable insights that drive decisions
Conduct product research (identify growth opportunities, answer high-level questions, and formulate hypotheses for the team)
Analyze user behavior patterns and independently uncover insights that are meaningful for the product
Influence goal setting and project prioritization within the team based on data
Develop self-service capabilities in the domain (structure data to make it easily usable by AI tools, and automate routine analysis so the team can independently answer basic questions)
Identifying growth opportunities for the product, analysis and sales forecasting, including identifying seasonality and trends
Requirements
5+ years of experience in data analysis
Ability to write complex SQL queries (complex joins, window functions, CTEs, query optimization)
Product-analytics depth (retention (paid & usage), cohort analysis, funnels, activation, ARPU/LTV, engagement)
Knowledge of mathematical statistics and practical experience applying it
Hands-on with a cloud warehouse (BigQuery or equivalent)
BI and data storytelling (builds and maintains dashboards (Metabase / Looker Studio or equivalent))
Skill in selecting appropriate data evaluation methods: analytics, research, or using external data
Ability to tackle complex problems in situations with incomplete data
Attention to detail and ability to see tasks through to completion
Preferred
Built a data-quality or data-observability system from scratch and owned it
Owned product analytics on a data / analytics product specifically
Python (pandas / numpy) for analysis and automation and PySpark / big-data processing for the more technical work
B2B SaaS experience
Experience in marketplace analytics and deep understanding of marketplaces base metrics and ranking algorithms
We offer
Compensation package: base salary and performance-based bonuses
Office-first: flexible hours with a possibility to work remotely 52 days per year, and 22 days of paid annual leave
Care & Wellbeing: health insurance (including dental care) for employees and their children, daily meal allowance, and 100% paid sick leave
AI-first approach: licenses and access to a wide range of AI tools
Team & Growth: collaboration with colleagues across Portugal, Brazil, Latvia and China, with opportunities for promotions, professional trainings, and English courses
Community & Engagement: annual team building activities, knowledge-sharing workshops, and a strong sense of team work
B
efore applying for the above position please review our Candidate Privacy Notice
here
. By responding to the vacancy, you acknowledge that you have read our Privacy notice.
Не хотите искать каждый день?
Настройте подбор один раз, и подходящие вакансии будут сами приходить в Telegram.
Настройте подбор, и подходящие вакансии будут приходить в Telegram.
ГибридKazakhstan; Kyrgyzstan; Uzbekistan; Hybrid-first, with fully remote work available for eligible roles and core collaboration hours from 12:00 to 19:00 Manila time (UTC+8); Relocation and family support; Relocation support for you, your family — including flights, two weeks of accommodation, visas, paperwork, and logistics; Reimbursement of eligible pet relocation expenses
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