Senior Data Engineer - Nebius

Design, build, and own production-grade data pipelines using Python and SQL. Develop stateless, idempotent pipelines that are resilient to retries, failures, and infrastructure interruptions. Implement data transformations, validation, and data quality checks. Optimize pipelines for performance, reliability, and cost efficiency. Collaborate closely with Analytics, Data Science, and ML teams to deliver trusted datasets. Orchestrate pipelines using a workflow orchestration framework (e.g., Airflow or equivalent). Package and run data workloads using Docker and deploy them on Kubernetes. Use autoscaling and Spot / Preemptible compute for efficient pipeline execution. Build CI/CD automation for data pipelines. Use Infrastructure as Code only to provision and manage the infrastructure required to run pipelines. 8+ years of experience as a Data Engineer, primarily focused on building data pipelines. 6+ years of hands-on experience with Python and SQL. 3+ years of experience running workloads on Kubernetes. Strong understanding of stateless system design and idempotent data processing. Experience building and operating data pipelines in cloud environments. Experience with workflow orchestration frameworks. Strong Linux fundamentals and production debugging skills. Working knowledge of spoken and written English Experience contributing to or working extensively with open-source software. Experience building data pipelines using Apache Spark or similar distributed processing frameworks. Experience building data pipelines that support machine learning workflows. Familiarity with cost-optimized data processing (e.g., Spot / Preemptible compute). Experience with relational and non-relational data stores. Experience working with large-scale or high-reliability data systems. Experience collaborating with strong Data Science and ML teams. Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams

Design, build, and own production-grade data pipelines using Python and SQL. Develop stateless, idempotent pipelines that are resilient to retries, failures, and infrastructure interruptions. Implement data transformations, validation, and data quality checks. Optimize pipelines for performance, reliability, and cost efficiency. Collaborate closely with Analytics, Data Science, and ML teams to deliver trusted datasets. Orchestrate pipelines using a workflow orchestration framework (e.g., Airflow or equivalent). Package and run data workloads using Docker and deploy them on Kubernetes. Use autoscaling and Spot / Preemptible compute for efficient pipeline execution. Build CI/CD automation for data pipelines. Use Infrastructure as Code only to provision and manage the infrastructure required to run pipelines. 8+ years of experience as a Data Engineer, primarily focused on building data pipelines. 6+ years of hands-on experience with Python and SQL. 3+ years of experience running workloads on Kubernetes. Strong understanding of stateless system design and idempotent data processing. Experience building and operating data pipelines in cloud environments. Experience with workflow orchestration frameworks. Strong Linux fundamentals and production debugging skills. Working knowledge of spoken and written English Experience contributing to or working extensively with open-source software. Experience building data pipelines using Apache Spark or similar distributed processing frameworks. Experience building data pipelines that support machine learning workflows. Familiarity with cost-optimized data processing (e.g., Spot / Preemptible compute). Experience with relational and non-relational data stores. Experience working with large-scale or high-reliability data systems. Experience collaborating with strong Data Science and ML teams. Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams

Design, build, and own production-grade data pipelines using Python and SQL. Develop stateless, idempotent pipelines that are resilient to retries, failures, and infrastructure interruptions. Implement data transformations, validation, and data quality checks. Optimize pipelines for performance, reliability, and cost efficiency. Collaborate closely with Analytics, Data Science, and ML teams to deliver trusted datasets. Orchestrate pipelines using a workflow orchestration framework (e.g., Airflow or equivalent). Package and run data workloads using Docker and deploy them on Kubernetes. Use autoscaling and Spot / Preemptible compute for efficient pipeline execution. Build CI/CD automation for data pipelines. Use Infrastructure as Code only to provision and manage the infrastructure required to run pipelines. 8+ years of experience as a Data Engineer, primarily focused on building data pipelines. 6+ years of hands-on experience with Python and SQL. 3+ years of experience running workloads on Kubernetes. Strong understanding of stateless system design and idempotent data processing. Experience building and operating data pipelines in cloud environments. Experience with workflow orchestration frameworks. Strong Linux fundamentals and production debugging skills. Working knowledge of spoken and written English Experience contributing to or working extensively with open-source software. Experience building data pipelines using Apache Spark or similar distributed processing frameworks. Experience building data pipelines that support machine learning workflows. Familiarity with cost-optimized data processing (e.g., Spot / Preemptible compute). Experience with relational and non-relational data stores. Experience working with large-scale or high-reliability data systems. Experience collaborating with strong Data Science and ML teams. Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams