Analytics Engineer

About the role

At Pluxee, we shape the world of employee benefits and engagement by bringing to life a personalized and sustainable employee experience, at work and beyond. Permanent Regular Job Description: Pluxee is a global player in Employee Benefits and Engagement that operates in 28 countries. Pluxee helps companies attract, engage, and retain talent thanks to a broad range of solutions across Meal & Food, Well-‏being, Lifestyle, Reward & Recognition, and Public Benefits. Powered by leading technology and more than 5,600 engaged team members, Pluxee acts as a trusted partner within a highly interconnected B2B2C ecosystem made up of more than 500,000 clients, 37 million+ consumers and 1.7 million+ merchants. Conducting business for more than 45 years, Pluxee is committed to creating a positive impact on local communities, supporting well-‏being at work for employees, and protecting the planet. Reporting to the Hub Lead Data Engineer, the Hub Analytics Engineer is a member of the Hub Data Analytics team. Perfect fusion of the Data Analyst and the Data Engineer, passionate about data and capable of handling end-to-end projects, he/she is responsible for the entire data lifecycle, from ingestion to reporting and analysis. With dual technical and business skills, the Analytics Engineer plays a crucial role in understanding business requirements, designing optimized data flows, and delivering robust and high-performance analytical model to improve commercial strategies and decision-making on multi-domain projects. The Analytics Engineer is key in Pluxee's Data & AI Transformation by acting as the bridge between raw data and business value. The Analytics Engineer is responsible for discovering, modeling, and delivering high-quality, reusable data assets that enable dashboards, self-service analytics, and AI applications across the organization. Mission & Responsibilities Expert in Design and Management of Data flows Develop and maintain robust, cost efficient, high-performance data pipelines, end-to-end, mixing several technologies like Azure Data Factory pipelines, Pyspark, Python, Rest API, SQL & NoSQL databases, batch and files... with different latencies (from batch to Realtime) Orchestrate jobs following DAG principles Ensure that data flows are reliable, documented, and easy to maintain Design optimized tables (choosing the right distribution, indexing types) to ensure maximum performance Implement proven patterns such as Slowly Changing Dimensions (SCD) or Data Vault to address needs related to data historization or flexibility Adopt established designs like Star Schema or relational models to facilitate analysis and reporting Document transformations and models comprehensively to ensure traceability and understanding by stakeholders Proficiency with Business Intelligence tools (Power BI) to ensure consistency between models and reporting Data Analyst closely embedded with Business teams: Actively participate in discussions with business teams to understand their specific data needs and decision-making processes Translate these requirements into concrete solutions, ensuring that the models delivered meet business objectives Conduct thorough data exploration to understand the relationships between different tables and identify relevant data sources to design data model structured correctly to support business goals Validate data quality and consistency against the provided business information by testing and auditing data to ensure it meets business rules and expectations for reliability: checking aggregations, verifying the integrity of table relationships, and managing errors or inconsistencies Facilitator in the iterative process of developing data flows to ensure that business expectations are clearly understood throughout the design process, adjusting models or data processes based on continuous feedback from business teams Data Analytics Platform User: Understand the data platform architecture and the purpose of each Azure resources Ensure compliance with data protection law (PII encryption) Monitor data ingestion jobs Develop, test, and deploy code and jobs within a DevOps framework, expertly managing branches, pull requests, and test environments Collaborate with other Analytics Engineers and Data Engineers and follow the guidelines set by the Hub team, ensuring alignment with our highest standards and best practices Capacity to read and understand code developed by other members to allow maintenance and evolution (reverse-engineering) Maintain and update project boards with accurate, up-to-date information, providing a clear view of project development progress and milestones Data Science skills (as a plus): Proficiency in Python or R for advanced data analysis and modelling Experience in developing and deploying predictive models (e.g., regression, classification, time series forecasting) to address business challenges Knowledge of statistical methods for data analysis, hypothesis

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