Senior Analytics Engineer

Vestiaire Collective is the leading global platform for desirable pre-loved fashion and a pioneer in transforming how people consume fashion.

Our mission is simple: make circular fashion the norm, not the exception. Through technology, expertise, and a highly engaged global community, we enable millions of people to buy and sell fashion in a more sustainable way.

Founded in Paris in 2009, Vestiaire Collective is now a globally scaled marketplace with offices in Paris, London, Berlin, New York, Singapore, and Ho Chi Minh City, and logistics hubs across Europe, Asia, and the US.

Today, we are a team of around 600 people from over 50 nationalities, united by a shared ambition: to drive meaningful change in the fashion industry.

Our values, Activism, Transparency, Dedication, Greatness, and Collective, shape how we build, collaborate, and grow every day.

About the role :

As a Senior Analytics Engineer, you will play a key role in our data ecosystem by modelling raw datasets into clear, reliable, and scalable data products that power strategic decision-making across the business.

Embedded within the central Analytics Engineering team, you will own and evolve data pipelines and analytical data products that support teams across Product, Marketing, CRM, Finance, Operations, and other areas of the business.

Beyond core development, you will drive data reliability, elevate our technical standards, and experiment with AI-assisted tools to streamline modern data workflows.

What you will do:

Design, build, and maintain scalable, high-performance data pipelines and reusable analytical data products using dbt, Snowflake, and Airflow.

Transform complex raw data into structured, reliable, and accessible data models, supported by appropriate testing and documentation, that enable decision-making across the organisation.

Own analytical data products throughout their lifecycle, from initial design and delivery through continuous improvement, evolution, and retirement, while considering performance, maintainability, and cost.

Contribute to the evolution of the engineering foundations supporting Analytics Engineering, including CI/CD, orchestration, monitoring, deployment, and tooling version upgrades, in collaboration with Data Platform Engineering.

Partner with Finance, Operations, Commercial, Marketing, CRM, Product, and other teams to translate business needs into clear, reliable, and scalable data solutions, applying technical judgement to clarify requirements and recommend effective approaches.

Take ownership of projects from design through delivery and communicate technical decisions and trade-offs clearly.

Explore and apply AI-assisted engineering tools across the data development lifecycle, including ingestion, transformation, testing, documentation, and deployment.

Help define and embed Analytics Engineering best practices, fostering a culture of quality, ownership, documentation, testing, and continuous improvement across the team.

Who you are :

3-5 years of relevant experience in Analytics Engineering, Data Engineering, BI Engineering, or a similar role, with hands-on experience designing, deploying, and maintaining large-scale, efficient ETL, ELT, or reverse ETL pipelines.

Strong SQL and data transformation skills(DBT), including complex transformations and modular, reusable data models.

Experience with Snowflake, BigQuery, PostgreSQL, or another modern data warehouse, with awareness of performance and cost considerations.

Experience with Airflow or a comparable tool for job orchestration, scheduling, and dependency management.

Experience using Python for data processing, workflow automation, or developing data tooling.

Experience with version controlling (GitHub), CI/CD, automated testing, monitoring, and observability.

Ability to work autonomously, manage dependencies, and take accountability for data products and technical initiatives beyond their initial delivery.

Ability to make and clearly communicate technical decisions and trade-offs.

Strong communication skills and the ability to work effectively with technical and non-technical stakeholders.

Practical experience or strong interest in applying AI-assisted engineering tools to data development.

Strong problem-solving mindset and commitment to engineering quality.

Prior exposure to e-commerce platforms, two-sided marketplaces, or high-volume AdTech ecosystems is a plus.