2 months ago
Remote, EMEA or Prague, CzechiaSenior
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 technical plans.
- Design and evolve data architecture, storage, processing, and orchestration patterns for large-scale workloads.
- Improve data quality, observability, lineage, monitoring, alerting, and incident response for critical datasets and pipelines.
- Investigate and resolve production performance, reliability, and data-correctness issues.
- Build reusable tools, conventions, and automation to improve engineering productivity and reduce operational risk.
- Collaborate with product teams and business stakeholders on data contracts, priorities, and success criteria.
- Contribute to design reviews, technical decisions, trade-offs, and documentation.
- Support and mentor engineers through reviews, pairing, and knowledge sharing.
- Participate in the on-call rotation and improve the operational health of supported systems.
Requirements
- 5+ years of experience in data engineering, backend engineering, or a related role, or equivalent experience delivering and operating production data systems.
- Proven ability to independently deliver complex data pipelines or data-platform capabilities from problem definition through production operation.
- Strong Python and SQL skills, including maintainable production code and optimization of non-trivial queries.
- Hands-on experience with workflow orchestration tools such as Airflow, Prefect, or Dagster.
- Strong understanding of data modeling, analytical models, and data contracts for multiple consumers.
- Solid knowledge of data architectures and storage systems, including processing and storage trade-offs.
- Experience with testing, monitoring, data-quality validation, alerting, debugging, and incident resolution for reliable systems.
- Ability to make technical decisions under ambiguity, explain trade-offs, and collaborate with technical and non-technical stakeholders.
- Preferred experience with real-time or event-driven data platforms and streaming technologies.
- Preferred experience operating cloud-native services with Docker and Kubernetes.
- Preferred familiarity with Infrastructure as Code, particularly Terraform.
- Preferred experience with data governance, access control, privacy, GDPR, or SOC 2 requirements.
- Preferred experience with data observability and quality tools or frameworks such as Great Expectations.
- Preferred experience improving engineering standards through shared libraries, platform tooling, technical documentation, or mentoring.
- Coding interviews are part of the hiring process.
Benefits
- 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.
Tech Stack
Categories
Data Engineering
About Nebius
Nebius builds a full-stack AI cloud offering GPU compute, storage, and tools for training and deploying ML models for startups, enterprises, and research labs. It sells consumption-based cloud infrastructure (IaaS/PaaS) and managed services tailored to generative AI workloads, including large-scale model training and inference. The company is headquartered in Amsterdam and operates as an independent provider.