EU remote
Senior Data Analyst (Hardware Automation)
About this role
About Nebius: 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 role The Hardware Infrastructure Automation team at Nebius operates at the intersection of physical infrastructure and intelligent software systems. We build the data layer that drives automation and operational decisions for our global hardware fleet.
We are looking for a Senior Data Analyst who can work end-to-end — from data modelling and pipeline engineering to dashboards and business recommendations. You will be a full owner of analytical projects: defining the problem, building the solution, and driving adoption with stakeholders. You will work closely with SWE, SRE, and frontend teams to surface insights that directly influence how we scale and automate our infrastructure.
If you are equally comfortable writing Python and SQL as you are presenting findings to leadership, this role is for you. Your responsibilities will include: End-to-end project ownership . Lead analytical projects independently from scoping and data modelling through to delivery and iteration. Define success metrics, manage timelines, and communicate progress without supervision. Data engineering . Build and maintain reliable data pipelines using Python and SQL.
Validate sources, monitor data quality, improve freshness, and ensure models are well-documented and reusable by the team. Dashboard development . Design and implement dashboards that give SWE, SRE, and frontend teams clear visibility into fleet health, utilisation, capacity, and automation coverage. Own dashboard quality and iteratively improve based on user feedback. Infrastructure analytics . Analyse large-scale hardware telemetry and operational data to identify bottlenecks, anomalies, and optimisation opportunities.