Product Data Analyst

The role

Nebius is looking for a Product Data Analyst. In this role, you’ll build and maintain analytical assets that support day-to-day decision-making across the product & business domains. You’ll work end-to-end: from shaping raw data into reliable datasets, to creating dashboards stakeholders actually trust to running deeper analyses that clarify what’s happening and what to do next. You’ll also help develop AI-assisted analytics tools that reduce manual reporting and surface anomalies early.

You’re welcome to work in our office in Amsterdam

Your responsibilities will include:

Master Product Analytics data and metrics & provide insights & consultancy to stakeholders and senior Management. from Product & Business domains. Act as their analytical thought partner and data focal point.

Dashboard development. Design, implement, document, and maintain interactive dashboards used by business stakeholders. Improve existing dashboards by optimizing usability, performance, and metric definitions.

Data engineering support. Contribute to ETL/ELT processes and data modeling: validate sources, monitor data freshness, improve data quality checks, and troubleshoot pipeline issues with clear root-cause writeups.

AI-driven analytics. Help design, test, and refine analytical AI agents that streamline reporting, standardize repetitive analysis, and proactively flag anomalies. Participate in evaluation (quality checks, false positives/negatives) and iterate based on feedback.

Exploratory and ad-hoc analysis. Translate business questions into analysis plans, apply appropriate statistical methods, and communicate findings clearly (including assumptions, limitations, and recommended actions). Turn recurring ad-hoc requests into reusable assets where possible.

Stakeholder collaboration. Clarify requirements, align on definitions (KPIs/metrics), and present results in a way that supports decisions—not just “interesting numbers.”

We expect you to have:

+4 years of experience as a product / business / data analyst

Strong proficiency in Python and SQL, and confidence using Excel when it’s the fastest tool for the job. You can write readable queries/code and validate your outputs.

Solid grounding in statistics and analytical thinking: descriptive statistics, hypothesis testing, regression fundamentals, and the ability to choose the right approach for the question.

Experience producing clear, accurate visualizations and explaining what matters in plain language.

High attention to detail and data quality mindset: you notice inconsistencies, validate definitions, and build checks instead of relying on hope.

Structured problem solving: Turns vague business questions into clear metrics, hypotheses, and actionable analysis.

Ownership & accountability: Takes work end-to-end, follows through, and proactively communicates progress/risks.

Clear stakeholder communication: Explains insights simply, aligns definitions, and delivers concise summaries people can act on.

Communication : Working knowledge of spoken and written English

It will be an added bonus if you have:

Experience with BI tools (e.g., Tableau, Power BI, Looker, Superset).

Familiarity with modern data stacks (DBT, Airflow, BigQuery/Snowflake/Postgres, etc.).

Comfort with experiment analysis / cohort analysis / funnel metrics