Data Engineering Manager - Paris

🇫🇷 Paris, FR Presencial TI Publicado Abr 1, 2026

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LocalizaçãoParis, FR

ModalidadePresencial

CategoriaTI

Categoria ITGestão de engenharia

Publicado1 de Abril de 2026

Última verificação6 de Maio de 2026

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 You will join the Data Platform team at Vestiaire Collective as an Engineering Manager, leading a team of 2 to 3 Senior Data Engineers. We are a lean, collaborative team responsible for the ingestion, transformation, and ML infrastructure that powers the entire organization. Our strategy is built on three pillars: Governance Excellence, Platform Enablement, and AI Innovation. We have built a strong self-serve foundation, and we are now entering the next phase of our journey: scaling our platform, improving efficiency, and preparing our infrastructure for the future of AI. In this role, you will combine people leadership, technical direction, and delivery ownership. You will support your team’s growth while ensuring we build a robust, scalable, and future-ready data platform. What You’ll Do 1. Lead & Grow the Team Manage and support a team of 2 to 3 senior Data Engineers, providing regular feedback, coaching, and career development. Foster a collaborative, high-performing, and accountable team environment. Ensure strong ownership, clarity of priorities, and high engineering standards. 2. Own Data Platform Reliability & Foundations Drive the reliability, scalability, and evolution of our core data infrastructure (Spark, Kafka, transformation layers). Define and enforce best practices around data quality, observability, and monitoring. Ensure the platform remains trusted, stable, and scalable as usage grows. 3. Drive Platform Enablement & Efficiency Lead initiatives to improve performance and optimize costs (FinOps mindset). Own the evolution of orchestration tools (Airflow) and ensure a smooth developer experience for data consumers. Partner with Analytics Engineers and Data Scientists to continuously improve the platform’s usability. 4. Enable AI & Future Innovation Define and support the infrastructure strategy for AI and ML use cases. Enable scalable solutions for ML workflows, model deployment, and LLM integration. Anticipate future needs and ensure the platform is ready for AI-driven products and operations. 5. Contribute to Technical Direction Guide architectural decisions and ensure pragmatic, maintainable solutions. Stay close to the tech: participate in design discussions and support complex problem-solving. Act as a bridge between engineering, data, and product stakeholders. Who You Are People Leader: You enjoy growing engineers and building strong, autonomous teams. Technical Anchor: You have a strong data engineering background and can guide technical decisions. Pragmatic: You focus on impact, scalability, and maintainability over complexity. Collaborative: You work effectively across teams and value shared ownership. Product & User-Oriented: You care about the experience of internal users (Data Scientists, Analysts, Engineers). Forward-Thinking: You are curious about the evolution of data platforms, AI, and modern data ecosystems. Your Skills We know you may not have experience with every tool listed below. Strong fundamentals and leadership matter most. Core Engineering & Data Strong experience with Python and SQL Solid experience with distributed data processing (Spark, Kafka) Platform & Infrastructure Experience with workflow orchestration (Airflow) Familiarity with AWS, Docker, Kubernetes Experience working with modern data stacks (e.g., Snowflake, dbt) Leadership Previous experience managing or mentoring engineers Ability to drive technical decisions and prioritize effectively MLOps & AI (plus) Experience with ML pipelines and tools (MLflow, SageMaker) Exposure to LLM/GenAI infrastructure (e.g., Vector DBs, Bedrock) Nice-to-Haves Experience building internal data platforms Familiarity with Infrastructure as Code (Terraform) Exposure to FinOps and cost optimization practices Tech Stack Python, Spark, Kafka, Airflow, Kubernetes, Snowflake, and modern ML platform tools (e.g., MLflow, SageMaker)