Vacancy description
Imported 2026-11-06 16:15:32.941029+00:00
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
Benefits & Perks:
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
What's it like to work at Nebius:
Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI
Equal Opportunity Statement:
Full-text baseline
Classified 2026-11-07 12:47:23.087540+00:00
DataNeeds review
Explicit role/task wording or requested domain skills support the proposed scope; LTA review remains required.
Skills suggested
- Information reporting
Source passage
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.
Proposed e-CF links
Suggested links for review. They do not assess skill levels.
Information reporting to D.10: Information and Knowledge Managementrelated · draft aiet for amey review
Source passage
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.
Task: The task maintains or structures organisational information.
Other source passages
Product Data Analyst
AI suggestion
Classified 2026-11-07 16:23:07.367086+00:00
DataAISuggestion
Skills suggested
- Information reporting, required
Source passage
Master Product Analytics data and metrics & provide insights & consultancy to stakeholders and senior Management. from Product & Business domains.
- SQL data queries, required
Source passage
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.
- Python programming, required
Source passage
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.
- Spreadsheet data work, required
Source passage
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.
- Statistical reasoning, required
Source passage
Solid grounding in statistics and analytical thinking: descriptive statistics, hypothesis testing, regression fundamentals, and the ability to choose the right approach for the question.
- Data visualisation, required
Source passage
Experience producing clear, accurate visualizations and explaining what matters in plain language.
- Data cleaning and quality, required
Source passage
High attention to detail and data quality mindset: you notice inconsistencies, validate definitions, and build checks instead of relying on hope.
- Data analysis, required
Source passage
Structured problem solving: Turns vague business questions into clear metrics, hypotheses, and actionable analysis.
- Needs and results communication, required
Source passage
Clear stakeholder communication: Explains insights simply, aligns definitions, and delivers concise summaries people can act on.
- Business intelligence reports, preferred
Source passage
Experience with BI tools (e.g., Tableau, Power BI, Looker, Superset).
- Data pipelines, preferred
Source passage
Familiarity with modern data stacks (DBT, Airflow, BigQuery/Snowflake/Postgres, etc.).
- AI system evaluation, required
Source passage
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.
Proposed e-CF links
Suggested links for review. They do not assess skill levels.
Information reporting to D.10: Information and Knowledge Managementrelated · draft aiet for amey review
Source passage
Master Product Analytics data and metrics & provide insights & consultancy to stakeholders and senior Management. from Product & Business domains.
Task: The task maintains or structures organisational information.
SQL data queries to D.7: Data Science and Analyticsrelated · draft aiet for amey review
Source passage
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.
Task: The queries extract or combine data for an analytical question.
Python programming to B.1: Application Developmentrelated · draft aiet for amey review
Source passage
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.
Task: The Python code implements a specified application task with tests.
Statistical reasoning to D.7: Data Science and Analyticsrelated · draft aiet for amey review
Source passage
Solid grounding in statistics and analytical thinking: descriptive statistics, hypothesis testing, regression fundamentals, and the ability to choose the right approach for the question.
Task: The task applies a statistical method to support data analysis.
Data visualisation to D.7: Data Science and Analyticsrelated · draft aiet for amey review
Source passage
Experience producing clear, accurate visualizations and explaining what matters in plain language.
Task: The display communicates an analytical result.
Data cleaning and quality to D.7: Data Science and Analyticsrelated · draft aiet for amey review
Source passage
High attention to detail and data quality mindset: you notice inconsistencies, validate definitions, and build checks instead of relying on hope.
Task: The task prepares data for analysis.
Data cleaning and quality to D.10: Information and Knowledge Managementrelated · draft aiet for amey review
Source passage
High attention to detail and data quality mindset: you notice inconsistencies, validate definitions, and build checks instead of relying on hope.
Task: The task applies quality controls within an organisational information process.
Data analysis to D.7: Data Science and Analyticsrelated · draft aiet for amey review
Source passage
Structured problem solving: Turns vague business questions into clear metrics, hypotheses, and actionable analysis.
Task: The task uses analytical techniques to address a defined organisational question.
Needs and results communication to D.11: Needs Identificationrelated · draft aiet for amey review
Source passage
Clear stakeholder communication: Explains insights simply, aligns definitions, and delivers concise summaries people can act on.
Task: The task clarifies customer needs for an ICT solution.
Business intelligence reports to D.7: Data Science and Analyticsrelated · draft aiet for amey review
Source passage
Experience with BI tools (e.g., Tableau, Power BI, Looker, Superset).
Task: The report provides analytical measures or insight.
Business intelligence reports to B.1: Application Developmentrelated · draft aiet for amey review
Source passage
Experience with BI tools (e.g., Tableau, Power BI, Looker, Superset).
Task: The task implements and validates a report application.
Data pipelines to B.2: Component Integrationrelated · draft aiet for amey review
Source passage
Familiarity with modern data stacks (DBT, Airflow, BigQuery/Snowflake/Postgres, etc.).
Task: The task integrates systems or components and checks compatibility.
Data pipelines to D.7: Data Science and Analyticsrelated · draft aiet for amey review
Source passage
Familiarity with modern data stacks (DBT, Airflow, BigQuery/Snowflake/Postgres, etc.).
Task: The task extracts and integrates data sources for analytics.
AI system evaluation to B.3: Testingrelated · draft aiet for amey review
Source passage
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.
Task: The task applies systematic tests to assess an ICT system against defined requirements.
Other source passages
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.
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.