Data engineer in AI infrastructure

The Data Engineering team builds and operates the data platform that powers analytics, business intelligence, operational reporting, and data-driven products across Nebius. We ingest data from internal and external systems, develop reliable transformation pipelines and data models, and make trusted datasets available to business and product teams.

• Own the design, delivery, and operation of complex data pipelines, datasets, and platform components.
• Translate business and analytical requirements into scalable data models, reliable data products, and clear technical plans.
• Design and evolve data architecture, storage, processing, and orchestration patterns for large-scale workloads.
• Improve data quality, observability, lineage, and incident response for critical datasets and pipelines.
• Investigate and resolve challenging performance, reliability, and data-correctness issues in production.
• Establish reusable tools, conventions, and automation that improve engineering productivity and reduce operational risk.
• Work with product teams and business stakeholders to define data contracts, priorities, and success criteria.
• Contribute to technical direction through design reviews, thoughtful trade-offs, and documentation.
• Support and mentor other engineers through reviews, pairing, and knowledge sharing.
• Participate in the on-call rotation and take ownership of improving the operational health of the systems you support.

• 5+ years of experience in data engineering, backend engineering, or a related role; or equivalent experience delivering and operating production data systems.
• Proven experience independently delivering complex data pipelines or data-platform capabilities from problem definition through production operation.
• Strong Python and SQL skills, including writing maintainable production code and optimizing non-trivial queries.
• Hands-on experience with workflow orchestration tools such as Airflow, Prefect, or Dagster.
• Strong understanding of data modeling, including designing maintainable analytical models and data contracts for multiple consumers.
• Solid knowledge of data architectures and storage systems, including the trade-offs between different processing and storage approaches.
• Experience designing for reliability: testing, monitoring, data-quality validation, alerting, debugging, and incident resolution.
• Ability to make technical decisions under ambiguity, explain trade-offs clearly, and collaborate effectively with engineers and non-technical stakeholder
• Будет плюсом: Experience with real-time or event-driven data platforms and streaming technologies; Experience building or operating cloud-native services with Docker and Kubernetes; Familiarity with Infrastructure as Code, particularly Terraform; Experience with data governance, access control, privacy, and compliance requirements such as GDPR or SOC 2; Experience with data observability and quality tools or frameworks, such as Great Expectations; Experience improving engineering standards through shared libraries, platform tooling, technical documentation, or mentoring

• 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
• Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI