Data Engineer (Middle+/Senior)

Компания "ТРТ"

A Stealth-mode AI-powered Cloud-Native Health-Tech is looking for a strong Middle+ Data Engineer to build and maintain scalable, cost-efficient data pipelines within a modern cloud analytics stack.

Candidates will independently implement production-grade ELT/ETL processes, optimize OLAP workloads, and contribute to data modeling initiatives under architectural guidance from senior engineers. This role requires advanced SQL proficiency, solid Python engineering skills.

Compensation and Benefits:

Paid Time Off

The company has Unlimited PTOs Policy and compensated New Years Holidays on top of that. The misuse of the policy isn’t welcomed, though it’s definitely possible to take at least two weeks – and fully compensated – vacation, or more.

Location and timezone

• We are focused on hiring in time zones overlapping with the US or Western Europe. Also considering additional locations where time zone overlap and payroll compliance can be reliably supported, including certain Eastern European and Middle Eastern countries (Bulgaria, UAE, etc.).

Some Bits of Stack

• Google Cloud Platform (GCP)

• GSuite

• LLMs and GenAI

• BigQuery

• Cloud Composer (Airflow, DBT)

• Python (FastAPI, pytest)

• Cloud Spanner

• Google Kubernetes Engine (GKE)

• Pub/Sub

• Git (GitHub, GitFlow)

• Google Cloud Build

• Terraform

• SonarSource

• LucidChart

Responsibilities:

Data Pipeline Development

• Design and implement reliable ELT/ETL pipelines from development to production.

• Develop incremental loading strategies for large-scale datasets.

• Ensure data quality, consistency, and observability within pipelines.

Data Modeling

• Develop and maintain DBT models following best practices.

• Work with structured and semi-structured data (JSON, arrays, nested fields).

• Optimize transformations for analytical workloads in BigQuery.

Platform & Engineering Practices

• Write modular, testable, and maintainable Python and SQL code.

• Follow best practices for version control, CI/CD, and documentation.

• Participate in code reviews and contribute to continuous improvement of engineering standards.

Requred experience:

Data Engineering

• 3- 5 years of hands-on experience in data engineering.

• Strong SQL skills (complex joins, window functions, aggregations, nested data handling).

• Practical experience with DBT for data transformation and modeling.

• Solid Python knowledge for data processing and automation (OOP principles, clean code practices).

• Experience implementing incremental data processing patterns.

• Ability to design and support production-grade data pipelines.

Cloud & Infrastructure

• Experience with Google Cloud Platform (or similar cloud exp.)

• Familiarity with Cloud Composer (Airflow-based orchestration).

• Basic understanding of Docker.

• Experience working in a Unix-like development environment (macOS or Linux).

Fundamentals

Ability to work in an Iterative Development workflow. This role is about evolving solutions through Incremental Delivery rather than a Waterfall-style approach.

Experience collaborating with analysts to translate business requirements into data models, schemas, and pipelines.

Stronger knowledge of Python and SQL is always prioritized.

There are many other Experience Advantages a candidate may have, e.g., Kafka, Apache Beam (Dataflow) Streaming, Spark Streaming, Python’s asyncio, Terraform, etc.

Ability to reason about performance, scalability and cost of data solutions.

Компания, занимающаяся разработкой облачных технологий для здравоохранения на базе искусственного интеллекта, ищет сильного Middle+ Data инженера для долгосрочного сотрудничества. Это не виртуальный продукт: их платформа поддерживает сети врачей, обеспечивая более интеллектуальное, адаптированное к рискам и прогнозируемое лечение, которое улучшает реальные результаты лечения пациентов.

Вы присоединитесь к международной команде первоклассных профессионалов, которые с энтузиазмом создают продукты, улучшающие качество медицинских услуг.

Требуемый опыт:

• 3–5 лет практического опыта в области инженерии данных.

• Навыки работы с SQL (сложные соединения, оконные функции, агрегации, обработка вложенных данных).

• Практический опыт работы с DBT для преобразования и моделирования данных.

• Глубокие знания Python для обработки данных и автоматизации (принципы ООП, практики чистого кода).

• Опыт внедрения инкрементальных моделей обработки данных.

• Способность проектировать и поддерживать производственные конвейеры данных.

• Опыт работы с Google Cloud Platform (или аналогичным облачным сервисом).

• Знакомство с Cloud Composer (оркестрация на основе Airflow).

• Базовое понимание Docker.

• Опыт работы в Unix-подобной среде разработки (macOS или Linux).

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