Senior Data Engineer

• 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

• 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.

We conduct coding interviews as part of the process.

• 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

Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.

Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.

Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.

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.

We are looking for a Senior Data Engineer to own substantial parts of our data platform and deliver complex data products end to end. You will turn ambiguous business needs into pragmatic technical solutions, make architecture and implementation trade-offs, and improve the reliability, scalability, and usability of our data ecosystem.

You will work closely with product, platform, and business teams, helping them use data effectively while ensuring that our systems remain maintainable and trustworthy as Nebius grows.

Your responsibilities

• 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.

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