Senior Data Analyst
NebiusАмстердам
SeniorHybridКачество текста 4/5EN C2+
Data AnalystPythonSQLPostgreSQL14ч
Условия:
• Гибридный формат работы в офисе в Амстердаме;
• Возможность работать над impactful AI-проектами;
Задачи:
• 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;
• AI-driven analytics: help design, test, and refine analytical AI agents that streamline reporting, standardize repetitive analysis, and proactively flag anomalies;
• Exploratory and ad-hoc analysis: translate business questions into structured analysis plans, apply appropriate statistical methods, and communicate findings clearly;
• Stakeholder collaboration: clarify requirements, align on definitions, KPIs and metrics, and present results in a way that supports decisions;
Требования:
• Significant experience as a Data Analyst, Senior Data Analyst, Business Analyst, Product Analyst, or in a similar analytical role;
• Strong proficiency in Python and SQL, and confidence using Excel when it is the most efficient tool;
• Solid grounding in statistics: descriptive statistics, hypothesis testing, and regression fundamentals;
• Experience producing clear data visualizations and communicating findings in plain language;
• Strong attention to data quality and detail;
• Ability to translate business questions into structured analysis and actionable insights;
• Solid business acumen — connecting analysis to commercial or operational impact;
• Strong problem-solving skills, comfort with uncertainty and ambiguity;
• Strong written and verbal communication, stakeholder management;
• Working knowledge of spoken and written English.
Будет плюсом:
• Experience with BI tools: Tableau, Power BI, Looker, or Superset;
• Familiarity with modern data stacks: dbt, Airflow, BigQuery, Snowflake, or PostgreSQL;
• Experience with experiment analysis, cohort analysis, or funnel metrics;
• Гибридный формат работы в офисе в Амстердаме;
• Возможность работать над impactful AI-проектами;
Задачи:
• 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;
• AI-driven analytics: help design, test, and refine analytical AI agents that streamline reporting, standardize repetitive analysis, and proactively flag anomalies;
• Exploratory and ad-hoc analysis: translate business questions into structured analysis plans, apply appropriate statistical methods, and communicate findings clearly;
• Stakeholder collaboration: clarify requirements, align on definitions, KPIs and metrics, and present results in a way that supports decisions;
Требования:
• Significant experience as a Data Analyst, Senior Data Analyst, Business Analyst, Product Analyst, or in a similar analytical role;
• Strong proficiency in Python and SQL, and confidence using Excel when it is the most efficient tool;
• Solid grounding in statistics: descriptive statistics, hypothesis testing, and regression fundamentals;
• Experience producing clear data visualizations and communicating findings in plain language;
• Strong attention to data quality and detail;
• Ability to translate business questions into structured analysis and actionable insights;
• Solid business acumen — connecting analysis to commercial or operational impact;
• Strong problem-solving skills, comfort with uncertainty and ambiguity;
• Strong written and verbal communication, stakeholder management;
• Working knowledge of spoken and written English.
Будет плюсом:
• Experience with BI tools: Tableau, Power BI, Looker, or Superset;
• Familiarity with modern data stacks: dbt, Airflow, BigQuery, Snowflake, or PostgreSQL;
• Experience with experiment analysis, cohort analysis, or funnel metrics;