Permanent Regular Pluxee is a global player in employee benefits and engagement that operates in 31 countries. Pluxee helps companies attract, engage, and retain talent thanks to a broad range of solutions across Meal & Food, Wellbeing, Lifestyle, Reward & Recognition, and Public Benefits.

Powered by leading technology and more than 5,000 engaged team members, Pluxee acts as a trusted partner within a highly interconnected B2B2C ecosystem made up of more than 500,000 clients, 36 million consumers and 1.7 million merchants.

Conducting its business as a trusted partner for more than 45 years, Pluxee is committed to creating a positive impact on all its stakeholders, from driving business to local communities, to supporting wellbeing at work for employees while protecting the planet.

• Ingest data into the company data platform
• Enhance data quality following Data Governance principles
• Ensure compliance with data protection law
• Automate data computations (for Business intelligence and Machine learning purposes)
• Feed operational system used by end users (Marketing, CRM, Sales solutions)
• Implement cost-efficient and industrialized data jobs on a hybrid infrastructure (on-premise / cloud) mixing several technologies: Rest API, SQL & No SQL databases, batch, and files… with different latencies (from batch to real-time).
• Actively participate in global projects involving your country: global data platform design and evolution, subsidiary data feedback processes, agile squad features tracking and coordination meetings, and stand-up.

• Master’s degree in Computer Science or Data Engineering or equivalent.
• +3 years experience as a Data Engineer in a permanent position, working transversally in an international company and an agile organization.
• Ability to work autonomously but with team spirit and engagement in projects.
• Strong communication skills, enabling effective collaboration with diverse stakeholders.
• Problem-solving mindset with a proactive approach to identifying and addressing challenges.
• Adaptability and openness to continuous learning and improvement.
• Attention to detail and commitment to delivering high-quality work.
• Strong organizational skills, with the ability to manage multiple tasks and prioritize effectively.
• Cultural sensitivity and ability to work in a multicultural environment
• Customer-oriented mindset.
• Effective time management skills to ensure project deadlines are met.
• Creative thinking and innovation in developing new solutions and improving existing processes.
• Bilingual in English.

• Building and managing ETL and ELT processes, working with data lakes to optimize data loads, standardize data, and ensure data quality and protection.
• Modeling data warehouses in a distributed architecture, ensuring efficient storage, retrieval, and processing of large-scale data sets.
• Implementing data governance practices, ensuring compliance with data privacy regulations, and maintaining data security.
• Using tools like Azure Data Factory/Synapse, Talend, or AWS Glue for orchestrating ETL workflows.

• Building and optimizing SQL stored procedures, performing query tuning, and managing database performance.
• Developing Python components within a Spark environment, handling data parsing (XML, JSON), and integrating with machine learning pipelines (optional but advantageous).
• Working with distributed computing frameworks like Apache Spark, Databricks, or Hadoop for processing large datasets.
• Utilizing version control systems (e.g., Git) and collaborating in a team environment using Agile methodologies.

• Comprehensive knowledge of Dev Ops principles, including CI/CD pipelines using tools like Azure Dev Ops, Jenkins, or Git Lab CI, and infrastructure as code (Ia C) using Terraform, Cloud Formation, or ARM templates.
• Understanding of cloud architecture and services across major cloud platforms (AWS, Azure, GCP), including storage solutions, compute resources, networking, and security practices.
• In-depth understanding of REST APIs, including OAuth for authentication, handling pagination, and using Swagger/Open API for API documentation and design.
• Implementing and managing serverless architectures, using services like AWS Lambda, Azure Functions, or Google Cloud Functions.
• Debugging and monitoring applications using tools like Cloud Watch, Azure Monitor, or GCP Stackdriver

• Video call with Talent Acquisition Expert
• Video call with Data Spoke Manager
• Video call technical interview