Data Engineer - Nebius

Design, develop, and maintain scalable data pipelines. Build and optimize data infrastructure. Implement data quality monitoring and validation frameworks. Optimize data storage, processing, and query performance for large-scale datasets. Design and implement data models for analytics and reporting use cases. Develop tools and automation to improve data engineering workflows and productivity. Ensure data governance, security, and compliance standards are met. Participate in on-call rotation to support production data systems. 3+ years of experience in data engineering or related roles. Experience building and maintaining data pipelines using orchestration tools (e.g., Airflow, Prefect, Dagster). Strong proficiency in SQL and solid programming skills in Python. Experience with distributed data processing frameworks (e.g., Apache Spark, or similar). Knowledge of data modeling principles and best practices. Understanding of data architectures and storage systems. Experience with real-time data streaming platforms. Familiarity with Infrastructure as Code tools (Terraform, etc). Experience with containerization (Docker) and Kubernetes. Knowledge of data governance and privacy frameworks (GDPR, SOC2). Knowledge of data quality and observability tools (Great Expectations, etc.). Competitive compensation Career growth and learning opportunities Flexibility and work-life balance Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams

Design, develop, and maintain scalable data pipelines. Build and optimize data infrastructure. Implement data quality monitoring and validation frameworks. Optimize data storage, processing, and query performance for large-scale datasets. Design and implement data models for analytics and reporting use cases. Develop tools and automation to improve data engineering workflows and productivity. Ensure data governance, security, and compliance standards are met. Participate in on-call rotation to support production data systems.

Design, develop, and maintain scalable data pipelines. Build and optimize data infrastructure. Implement data quality monitoring and validation frameworks. Optimize data storage, processing, and query performance for large-scale datasets. Design and implement data models for analytics and reporting use cases. Develop tools and automation to improve data engineering workflows and productivity. Ensure data governance, security, and compliance standards are met. Participate in on-call rotation to support production data systems. 3+ years of experience in data engineering or related roles. Experience building and maintaining data pipelines using orchestration tools (e.g., Airflow, Prefect, Dagster). Strong proficiency in SQL and solid programming skills in Python. Experience with distributed data processing frameworks (e.g., Apache Spark, or similar). Knowledge of data modeling principles and best practices. Understanding of data architectures and storage systems. Experience with real-time data streaming platforms. Familiarity with Infrastructure as Code tools (Terraform, etc). Experience with containerization (Docker) and Kubernetes. Knowledge of data governance and privacy frameworks (GDPR, SOC2). Knowledge of data quality and observability tools (Great Expectations, etc.). Competitive compensation Career growth and learning opportunities Flexibility and work-life balance Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams