Vestiaire Collective - Data Engineering Manager

Berlin1w ago

In Office Staff EMEA Cloud Computing Artificial Intelligence Engineering Manager Data Engineer Co-op Team Management Governance Coaching Contract Management Kafka

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Requirements

• 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)

Responsibilities

• 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.

Benefits

• Purpose-driven work at scale • High-impact scope & ownership • Work on products used globally, where your decisions have immediate, measurable impact on millions of users across 70+ countries. • A truly international environment • Collaborate with a diverse team of 50+ nationalities across Paris, London, Berlin, New York, Singapore, and Ho Chi Minh City. • Career acceleration in a fast-moving scale-up • Take ownership early, grow fast, and shape your path, as an expert or a future leader. • Learning & growth as a priority • Dedicated budget, continuous feedback culture, and opportunities to work on cutting-edge topics (AI, marketplace dynamics, scalability, etc.). • Flexible ways of working • Hybrid model (typically 2 days remote per week), with trust and autonomy at the core of how we operate. • Give back through action • Including bonus, health coverage, lunch vouchers, Gym-Pass, and additional legal perks depending on your location. • Research shows that candidates from underrepresented backgrounds including women, people with disabilities, and other marginalized communities, are less likely to apply unless they meet 100% of the criteria. • At Vestiaire Collective, we believe diversity drives better decisions, stronger products, and more meaningful impact.