Analytics Engineer

Richemont, one of the world leaders in the luxury sector, has various Houses specializing in jewelry, watches and high-end accessories.   Each Maison proudly embodies a tradition of style, quality and craftsmanship and Richemont strives to preserve the heritage and identity specific to each of them.   At the same time, we are committed to innovating and designing new products in line with the values ​​of our Houses, through a process of permanent creativity.

MISSION

Reporting to the Data Enablement Manager, you will be a key member of the Business Performance team, leading initiatives that reinforce Richemont Europe’s position as a strong partner and business enabler for our Maisons. In this role, you will empower data-driven decision-making for our Functions & Maisons, through data enablement. You will be responsible for designing, building, and maintaining robust and scalable data models and pipelines that transform raw data into clean, reliable, and query-ready datasets for analysts and business users. You will ensure data integrity, performance, and accessibility, enabling efficient reporting and advanced analytics. As our organization evolves, you will also help build AI Agents and implement advanced automation solutions (AI/ML).

SCOPE - Richemont Europe Functions (RRF) & Markets.

KEY RESPONSIBILITIES & DUTIES

- Data Modeling: Design, develop, and maintain advanced data models that support various analytical needs across Functions & Maisons Europe.
- ETL/ELT Development: Build, optimize, and manage data transformation pipelines using SQL, Python, and other relevant tools to extract, load, and transform data from various source systems into the data warehouse/lake.
- Data Quality & Validation: Implement data quality checks, validation rules, and monitoring processes to ensure the accuracy, consistency, and reliability of data assets.
- Performance Optimization: Optimize data models and queries for performance and scalability, ensuring efficient data retrieval for reporting and analytical applications.
- Collaboration: Work closely with Data Scientists, Business Analysts, and Data Engineers to understand data requirements, translate business logic into technical specifications, and deliver high-quality data solutions.
- Access management: Create and manage access rules for our advanced data models efficiently.
- Documentation: Create and maintain comprehensive documentation for data models, pipelines, and data dictionaries.
- Tooling & Technology: Utilize and contribute to the development of modern data stack tools (e.g., dbt, BigQuery).
- Troubleshooting: Identify and resolve data-related issues, ensuring data availability and accuracy.
- Dashboarding: Develop, maintain and deploy dashboards answering identified business needs.
- Insights: Perform in depth analyses to address specific business needs by providing actionable insights.
- User upskilling: Train, accompany and support business users in using efficiently our analytics tools.
- Business planning: Actively support in the preparation of the budget, strategic plans, business reviews, business development exploration & operational forecasts.
- Projects: Be a key contributor in data related business projects to identify efficiencies, opportunities and eventual risks (ex. Commission scheme).
- Agentic AI: Collaborate with senior engineers and data scientists to design and implement AI Agents, by applying machine learning techniques, including Large Language Models (LLMs).
- Regional Forecasting Process: Drive for the regional forecasting process supporting the regional exco in the Budget preparation and the local logistics operations team in the capacity planning.

PROFILE

Required Skills & Experience:

- Strong proficiency in SQL for data manipulation, transformation, and querying.
- Experience with data warehousing concepts and technologies (e.g., Google BigQuery).
- Proficiency in a scripting language, preferably Python, for data processing and automation.
- Experience with data transformation tools, especially dbt (data build tool), is highly desirable.
- Familiarity with cloud platforms (e.g., GCP, AWS, Azure) and their data services.
- Understanding of data modeling techniques (e.g., Kimball methodology).
- Experience with version control systems (e.g., Git).
- Strong analytical and problem-solving skills with attention to detail.

- Ability to communicate effectively with both technical and non-technical stakeholders.
- Ability to manipulate and condense large volumes of data into succinct and powerful visualizations (graphs, charts, diagrams, dashboards, pivot tables)

Qualifications:

- Bachelor's or Master's degree in Computer Science, Engineering, Statistics, Mathematics, or a related quantitative field.
- Relevant certifications in cloud data platforms or data engineering are a plus.

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