Data engineer in payments

Grid Dynamics; Warszawa; On-site; PLN 180,000 - 240,000; Full time

Benefits offered by this job

Bleeding-edge проекты

Мотивированная команда

Конкурентная зарплата

Гибкий график

Медицинская страховка и спорт

Корпоративные мероприятия

Профессиональное развитие

Соврем офис

Job summary

Grid Dynamics ищет Data Engineer для работы над новой платформой данных в глобальной платежной компании. Вам предстоит развивать Databricks на AWS, проектировать слои Bronze/Silver/Gold и обеспечивать качество кода и данных.

В команде с тех-лидом и инженерами заказчика вы будете работать над обработкой потоков и аналитикой в реальном времени. Требования включают опыт разработки продюсерских конвейеров данных, Databricks, Spark, Python и SQL, а также знание AWS.

Qualifications

• Коммерческий опыт построения продюсерских конвейеров данных.
• Умение проектировать и оптимизировать конвейеры на Databricks.
• Знания Python и SQL и опыт работы с AWS.

Responsibilities

• Разрабатывать конвейеры и преобразования на Databricks в AWS, создавать Bronze/Silver/Gold слои.
• Оптимизировать нагрузки Spark/PySpark и обеспечивать качество данных.
• Проводить тестирование, писать модульные и интеграционные тесты, участвовать в ревью кода.
• Работать над интеграциями с Hadoop и Kafka, поддерживать низкую задержку.

Tools

Databricks

Spark

Python

SQL

ClickHouse

Kafka

Hadoop

AWS

Delta Lake

CI/CD

MLOps

Job description

Описание: We are seeking a Data Engineer (Databricks / Spark) to join a lean, engineering-focused team building a new data platform for a global payments technology company. In this role, you will work closely with our Tech Lead and the client’s engineers to build Databricks on AWS pipelines across a medallion architecture, develop ClickHouse structures for real-time analytics, and own the quality of your code end to end, from automated tests to data quality checks, while giving pragmatic support to a legacy Hadoop platform.

Задачи

Build pipelines and data transformations on Databricks on AWS, developing Bronze, Silver and Gold layers as part of a platform that unifies the engineering approach across many consumers and use cases. Develop and optimize Spark / PySpark workloads, including those behind monthly reports for around 40,000 merchants. Build ClickHouse ingestion and analytical structures for low-latency use cases. Implement automated tests, integration tests and data quality checks yourself, with no dedicated QA on the team, and take part in code reviews. Troubleshoot production issues and deliver targeted fixes in a legacy Hadoop platform with file-based sources and Kafka streams (around 10% of your time). Contribute to shared engineering patterns that become the standard for the new platform, with potential growth into AI/ML and MLOps.

Требования

Commercial experience in data engineering, building production data pipelines and transformations. Hands‑on experience building pipelines and workloads on Databricks. Strong practical skills in developing and optimizing Spark workloads. Strong programming skills in Python and SQL. Ability to own automated testing and data quality validation as part of everyday delivery. Будет плюсом: Strong experience building automated testing for data platforms; Knowledge of the Hadoop ecosystem, Kafka and streaming data; Experience with ClickHouse for real-time or low-latency analytics; Fundamentals of AI/ML and MLOps, including Databricks MLOps; Experience with AWS, Delta Lake, CI/CD and performance optimization

Условия

• Opportunity to work on bleeding‑edge projects
• Work with a highly motivated and dedicated team
• Competitive salary
• Flexible schedule
• Benefits package - medical insurance, sports
• Corporate social events
• Professional development opportunities
• Well‑equipped office