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описание
We are looking for a GCP Data Engineer to join a highly technical Data & AI engineering environment, working at the intersection of Software Engineering, Data Engineering and MLOps. You will design and build scalable data solutions, ML-enabled platforms and event-driven architectures, with a strong focus on engineering quality, automation and cloud-native development.
задачи
Design, develop and operate scalable data pipelines for analytics, reporting and Machine Learning use cases.
Build data and ML solutions using Python, PySpark, Databricks and MLflow.
Develop batch, streaming and event-driven architectures within a cloud environment.
Apply strong Software Engineering principles to Data Engineering, including clean code, testing, maintainability and scalability.
Optimize the performance and reliability of data engineering solutions.
Ensure data quality and consistency through validation, monitoring and automated processes.
Build and maintain CI/CD pipelines for data and software workloads.
Work primarily across GCP, while contributing to a multi-cloud environment including Azure.
Collaborate with Data Engineers, Software Engineers, Data Scientists, ML Engineers and Product teams.
Monitor and troubleshoot production data infrastructure and continuously improve platform reliability.
требования
Strong programming skills in Python, including solid knowledge of Object-Oriented Programming (OOP).
Hands-on experience with PySpark and/or Databricks.
Strong SQL skills and experience with platforms such as BigQuery and/or Snowflake.
Experience designing and developing production-grade data pipelines, including batch and/or streaming workloads.
Good understanding of GCP and cloud-native data architectures.
Exposure to multi-cloud environments, ideally GCP and Azure.
Experience with CI/CD, ideally using Azure DevOps and/or GitLab CI.
Strong understanding of Software Engineering and DevOps best practices.
Experience building scalable, maintainable and production-ready data solutions.
Будет плюсом: Experience with MLflow and MLOps practices; Docker and Kubernetes; Experience with LLMs / Applied AI, including frameworks such as LangChain; API development using FastAPI or Flask; Experience with event-driven and distributed architectures; Knowledge of data security, governance and privacy principles
