Data Analyst

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. Translate complex infrastructure signals into clear, actionable recommendations. - Automation insight. Support the team's automation roadmap by quantifying the impact of automation initiatives, identifying gaps in coverage, and modelling scenarios that inform prioritisation. - Stakeholder collaboration. Work directly with HW RnD, IT Operations, DC Operations, Logistics, and other teams to clarify requirements, align on metrics, and present results in a way that supports engineering decisions — not just "interesting numbers". - Analytical best practices. Raise the standard of analytical work in the team through documentation, metric definitions, code reviews, and mentoring less experienced colleagues where relevant. We expect you to have: - Significant experience as a Data Analyst, Senior Data Analyst, or in a similar role with strong technical ownership. - Strong proficiency in Python and SQL — you write clean, production-quality code, not just scripts. - Proven experience building and maintaining dashboards used by engineering or operations teams. - Experience owning analytical projects end-to-end: from data modelling and pipeline development through to stakeholder delivery. - Solid grounding in statistics and analytical thinking, including descriptive analytics, hypothesis testing, and regression fundamentals. - Strong attention to data quality, including validating definitions, identifying inconsistencies, and implementing monitoring. - Comfort working with large, complex, and sometimes ambiguous datasets — including telemetry, logs, or operational data. - Ability to communicate technical findings clearly to non-technical audiences, including senior leadership. - Strong problem-solving skills — you break down ambiguous questions, form hypotheses, and identify practical next steps independently. - Working knowledge of spoken and written English. It will be an added bonus if you have: - Experience with hardware infrastructure, data centre operations, or cloud infrastructure analytics. - Familiarity with modern data stacks such as dbt, Airflow, BigQuery, Snowflake, or PostgreSQL. - Experience with BI tools such as Tableau, Power BI, Looker, or Superset. - Background in automation analytics, capacity planning, or fleet management. - Experience mentoring other analysts or contributing to team standards and documentation.