Data Analyst

4. Collaboration & Governance Work closely with local and regional business stakeholders, the data community, and global data governance teams Ensure alignment with data governance standards and contribute to data quality and catalog initiatives 5. Technology & Tools Leverage R and/or Python for statistical analysis Utilize SQL for querying and transforming large datasets Ensure optimal data model structures (e.g., star schema, SCD patterns) for dashboard readiness YOU ARE A MATCH IF...

Education: Master’s degree in Statistics, Applied Mathematics, Data Science, or related fields Work Experience: Minimum 2 years in a similar role, preferably in a multinational or digital-first environment Experience with business intelligence tools (e.g., Power BI) and statistical software (e.g., R, Python) Skills & Certifications: Strong expertise in SQL and database optimization Solid foundation in KPI design and business performance indicators Experience with data storytelling and communicating technical findings to non-technical audiences Familiarity with hybrid data architectures (on-prem/cloud) is a plus

Work Experience: Minimum 2 years in a similar role, preferably in a multinational or digital-first environment Experience with business intelligence tools (e.g., Power BI) and statistical software (e.g., R, Python) Skills & Certifications: Strong expertise in SQL and database optimization Solid foundation in KPI design and business performance indicators Experience with data storytelling and communicating technical findings to non-technical audiences Familiarity with hybrid data architectures (on-prem/cloud) is a plus Languages: For global, regional, or area roles, fluency in English is required. Additional languages such as French, Spanish, Portuguese, or other relevant languages are an advantage. For local country leadership and management roles, fluency in the local language and English is required. For local individual contributor roles, fluency in the local language is required. English is considered an advantage as it supports communication, learning, and collaboration in a multinational context.

Working Conditions: For global, regional, or area roles: Hybrid working patterns depending on the host country policies. Collaboration across different countries, functions, and time zones. Travel may be required depending on business needs. For local roles: Hybrid working environment depending on local policies. Collaboration with local business teams and relevant regional or global stakeholders. Occasional travel may be required depending on business needs. Competencies: Data Analysis Statistical Modeling SQL & Database Management Data Visualization Business Acumen Why join Pluxee?

The Data Analyst plays a critical role in Pluxee’s decentralized Data Hub & Spoke model. Reporting to the Data Spoke Manager and embedded in a local or regional data team, this position supports decision-making and strategic insight generation across multiple countries. The role transforms data into actionable intelligence to improve operational performance, support business growth, and advance our understanding of clients, consumers, and merchants across Pluxee’s B2B2C ecosystem. Responsibilities: 1. Business Partnership & Decision Support Collect, challenge, and translate business needs into analytical questions and data solutions Provide insight-driven recommendations to business teams for improved decision-making Deliver business performance analysis with relevant KPI computation and benchmarking 2. Analytics & Reporting Produce and maintain operational dashboards and reports using Power BI Execute ad-hoc studies, translating complex datasets into meaningful insights Apply statistical modeling techniques (e.g., regression analysis, time series adjustment, control group sampling) to understand business drivers 3. Data Modeling & Visualization Design and optimize data visualizations and storytelling for stakeholder communication Collaborate with the Data Engineering team to ensure data structures support analytical goals Formalize business problems into analytical models and ensure robust documentation