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 testing, A/B testing, and understanding of concepts such as p-values, confidence intervals, and model evaluation metrics
Familiarity with neural networks and deep learning techniques (if applicable) to solve more complex business problems
This is your chance to be part of a dynamic team where your expertise in data platform management and project execution will drive innovation and contribute significantly to our business success. Your role is pivotal in ensuring the smooth and effective use of our data resources and in steering our projects towards their strategic objectives.
Profile, Experience & key Competencies
Master's degree in computer science/data engineering/statistical engineering/information processing or equivalent
At least 2 years building and managing ETL and ELT processes
Strong SQL skills: querying and procedures
Advanced knowledge of relational and dimensional database management (indexing, distribution choices)
At least one of the following languages: C#, Python or PySpark
Data modelling for Analytical purposes
Data modelling for OLTP databases
Cloud computing: storage, serverless, API … (Azure is a plus)
Distributed computation with Spark
Good understanding of python applied to Machine Learning
Devops principles with CI/CD (azure devops) and infrastructure as code
Rest API: Oauth, Swaggers, backend services
Real or near real-time data process design and implementation
Advanced knowledge of data lake and data warehouse for analytical and operational purposes
Experience in working transversally in an international company and in an agile organization
Agile Project Management Knowledge (tracking on Azure Boards)
Good understanding of company business
Ability to understand business needs and co-build solutions that meet those needs
Excellent communication skills, especially with non-technical stakeholders
Curiosity and proactive problem-solving approach
Fluency in English is required
Ability to work autonomously
Team spirit and engagement in projects
Working Conditions:
This is a full-time role.
Hybrid: on site and work from home according to hosting country policy
Joining Pluxee as a Hub Analytics Engineer offers an exciting opportunity to be at the forefront of our strategic initiatives. As a collaborative and innovative team, we value your expertise in driving successful project execution and making a significant impact on the organization's growth and success.
Why join Pluxee?
Join an inclusive team
Work at the heart of the community alongside 5,000 experts across 29 countries - we embrace diversity and value uniqueness, fostering a workplace where everyone can thrive.
Turn inspiration into possibility
Get space to try new ideas, experiment and move the world of work forward as you bring new digital experiences - and moments of connection - to millions of people.
Make a positive impact
Touch millions of lives and create meaningful change for people and the world we share, supporting our commitments to diversity, sustainability, and local economies.
Grow with us
Get support you need to take meaningful steps in your career, whether you're performing your work/life balance or learning new skills.
