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vestiairecollective
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Франция
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HeadГибридКонтракт

Data Engineering Manager

Оценка ИИ

Высокий балл за сильный бренд компании, социально значимую миссию (экология), современный технологический стек и международную среду. Позиция предлагает реальное влияние на стратегию AI в крупном масштабе.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Роль требует сочетания сильных лидерских качеств для управления опытными Senior-инженерами и глубокой технической экспертизы в современном стеке данных (Spark, Snowflake, Airflow). Дополнительную сложность добавляет фокус на MLOps и инфраструктуру для ИИ.

Анализ зарплаты

Медиана95 000 €
Рынок85 000 € – 115 000 €
Оценка ИИ

Предлагаемая роль менеджера в Париже соответствует высокому уровню рынка. Оценка базируется на данных для технологических компаний уровня scale-up во Франции, где компенсация для Engineering Manager обычно включает значительную бонусную часть.

Сопроводительное письмо

I am writing to express my strong interest in the Data Engineering Manager position at Vestiaire Collective. With a solid background in building scalable data platforms and a passion for circular fashion, I am eager to lead your team of senior engineers in scaling the infrastructure that powers your global marketplace. My experience with Spark, Airflow, and Snowflake, combined with a focus on FinOps and MLOps, aligns perfectly with your mission to drive AI innovation and governance excellence.

Throughout my career, I have successfully managed engineering teams by fostering a culture of accountability and continuous growth. I am particularly drawn to Vestiaire Collective's commitment to sustainability and transparency. I am confident that my technical expertise in distributed processing and my pragmatic approach to leadership will help your team deliver a robust, future-ready data platform that supports both analytics and advanced AI initiatives.

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Присоединяйтесь к лидеру ресейл-индустрии и возглавьте развитие современной платформы данных в Париже!

Описание вакансии

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)

What We Offer

  • Purpose-driven work at scale

Join a company reshaping the fashion industry towards circularity, you directly contributes to reducing waste and extending the life of luxury items.

  • 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

2 paid days per year to support a cause of your choice and actively contribute to positive impact beyond your day-to-day role.

  • Competitive compensation & benefits

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.

If this role excites you but your experience doesn’t align perfectly, we still encourage you to apply, your perspective could be exactly what we’re looking for.

*Vestiaire Collective is proud to be an equal opportunity employer.*

PS: We take candidate experience seriously, and your safety too !

Vestiaire Collective will only contact you through official email addresses ending in @vestiairecollective.com or no-reply@hire.lever.co.

We will never:

  • Contact you via WhatsApp, Telegram, or similar platforms
  • Ask for payment or banking information at any stage of the process

If you receive a suspicious message, please report it to: talentacquisition@vestiairecollective.com

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Навыки

  • AWS
  • Python
  • Terraform
  • SQL
  • dbt
  • Kubernetes
  • Docker
  • Snowflake
  • Airflow
  • Kafka
  • Spark
  • MLflow
  • SageMaker

Возможные вопросы на собеседовании

Проверка опыта управления высококвалифицированными специалистами и умения развивать их карьеру.

Как вы подходите к управлению и мотивации команды, состоящей исключительно из Senior-инженеров?

Оценка технического видения и понимания современных архитектурных трендов.

Каким вы видите идеальный процесс перехода от классической платформы данных к инфраструктуре, готовой к масштабному внедрению LLM и AI?

Проверка навыков оптимизации ресурсов, упомянутых в вакансии (FinOps).

Расскажите о вашем опыте оптимизации затрат на облачную инфраструктуру или хранилища данных (например, Snowflake). Какие метрики вы использовали?

Оценка способности балансировать между техническим совершенством и потребностями бизнеса.

Как вы расставляете приоритеты между задачами по устранению технического долга платформы и запросами от команд Data Science на новые фичи?

Проверка опыта работы с ключевыми технологиями стека компании.

С какими самыми сложными проблемами масштабирования Spark или Kafka вы сталкивались и как их решали?

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