Data Platform Engineer - Database & Infrastructure

Scaleway

OUR STORY:   🇪🇺 Join Scaleway and shape the sovereign cloud of tomorrow ! Since 1999, we have been designing secure, sustainable infrastructures aimed at supporting the most ambitious companies.   Historically known for our dedicated servers (Dedibox), we made a strategic shift to cloud computing in 2015. Staying true to our principles of simplicity, flexibility, and technical excellence, we have become one of the leading players in Europe in the sector.   With the rise of artificial intelligence, we have strengthened our commitment, supported by the Iliad Group, which is investing €3 billion to develop a serious, sovereign AI alternative to American and Asian giants.   Every day, thanks to our fast-growing portfolio of cloud and AI products (bare metal, containerization, serverless, AI, etc.), Scaleway proudly serves thousands of customer across the private and public sector, from corporations like France Télévisions or Hachette Livre, to fast-growing startups like Photoroom and Biolevate, to institutions like the City of Copenhagen.   📍 Our offices are located in Paris, Lille, Toulouse, Rennes, Rouen, Bordeaux and Lyon.   WHY WE NEED YOU? Our growth is driving us to strengthen our Database & Data Platform team to increase our capacity to deliver new features and improve the reliability and robustness of our managed databases service.   Your mission: Own and evolve both the internal and public‑facing managed‑database platform, driving scalability, security and long‑term performance while shaping the next generation of our cloud database offer.     YOUR FUTURE TEAM We work in a collaborative environment where knowledge sharing and mutual support are key to driving our ambitions forward.   You’ll join a tight‑knit team of six, distributed across our French sites: three developers, one DBA, one product manager, and one engineering manager. In this role you’ll write production‑grade code and build and maintain the infrastructure that runs it—from Python & Go‑based APIs to Ansible playbooks, CI/CD pipelines, observability stacks.    The team owns both an internal database platform that powers other Scaleway products and the public‑facing managed‑database service.   YOUR DAILY ROUTINE • Design, implement, and ship new features for the managed‑database service while continuously improving the existing codebase. • Maintain and evolve internal database platforms (new deployments, automated upgrades, performance optimizations, …). • Contribute to design reviews and propose improvements to internal architecture and tooling. • Improve the technical stack used for database management and automation. • Work with tools like Terraform, Ansible and Docker to support infrastructure and automation. • Collaborate with other developers and DBAs to guide database usage and best practices. • Diagnose and resolve incidents and bugs affecting databases • Participate in a second‑level on‑call rotation (approximately 1 week / month), stepping in when first‑level alerts need deeper investigation and ensuring rapid restoration of our managed‑database services • Resolve critical production issues, working closely with our SRE team to restore service and implement long‑term fixes.     ABOUT YOU HARD SKILLS: • Strong Development experience in Python or Go • Strong Linux system administration skills and experience with Bash scripting • Solid experience in administering at least one major RDBMS in production environments (ideally PostgreSQL or MySQL)   • Deep understanding of database administration mechanisms: high availability, security, access control, disaster recovery, and service continuity strategies. • Comfortable working with automation and infrastructure tools: Ansible, Docker, Git & CI/CD pipelines • Familiarity with monitoring tools such as Prometheus   SOFT SKILLS: • Excellent communication and synthesis skills: ability to explain technical concepts clearly to both technical and non-technical stakeholders • Strong sense of rigor and high standards, especially when working on critical infrastructure • Proactive and autonomous, with the ability to work effectively in a distributed team • Ability to collaborate with empathy and contribute to a caring, respectful work environment • Professional written communication in English; comfort in French-speaking teams   WHAT YOU WILL FIND AT SCALEWAY ++++ • Hybrid work: We offer up to 3 days of remote work per week. • Offices: Our offices are spacious, dynamic workspaces with bold design, conveniently located near public transport. Most of our offices feature outdoor spaces (terraces) and bike parking facilities. • Dining: Our chef provides a healthy meal service at the headquarters, and breakfast is available across all our sites year-round. Scalers working from regional sites enjoy a Swile card for lunches. • Well-being commitments: Whether it’s access to a gym, daycare places, or discounted services for caring services, Scaleway is committed to supporting Scalers in maintaining a balanced life. • International environment: With dozens of nationalities, Scaleway offers a stimulating environment where English is as widely spoken as French. • Career & Mobility: Our managers value internal mobility, and opportunities to transition to other entities within the Iliad Group are accessible to all Scalers.     WHY JOIN THE SCALEWAY ADVENTURE ? ✔ A rich and diverse product offering: Scaleway offers over 100 public cloud products in IaaS, PaaS, and AI. ✔ A cutting-edge technical environment: Scaleway provides modern infrastructures, including high-performance bare metal servers, to tackle exciting technical challenges. ✔ Commitment to responsible cloud: Scaleway is dedicated to a more responsible cloud, with data centers powered solely by renewable energy since 2017, minimizing our ecological footprint and holding top-level certification.   THE NEXT STEPS … • Discovery call with a recruiter (30 min) • Interview with Manager (30 min) • Technical interview (1h min) • Interview with Head of Tribe (30 min)

Qube Research & Technologies

Programme duration: from 5 to 6 months, starting in 2026.    Who qualifies: Penultimate or final year students completing a Bachelor's, Master's.   Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped our collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.   Over the years, QRT has invested in a global research and execution platform which has been deployed to cover all geographies and asset classes. This platform covers a broad spectrum from high to low frequency trading systems. We thrive at the intersection of cutting-edge technology, smart automation, and scalable processes, enabling us to move fast, think big, and deliver at scale.    We are committed to identifying and developing exceptional talent, and are inviting a new cohort of outstanding individuals to join us in the year ahead. Our internship offers a stimulating, intellectually rigorous, and high-performance environment, where collaboration is key to success. You will work alongside and be mentored by industry-leading professionals, gaining invaluable experience and positioning yourself for the opportunity to secure a full-time graduate role upon successful completion of the program.     Your future role at QRT   Throughout the recruitment process, we will work to align your skills, interests, and potential with the teams where you can make the greatest impact. As part of QRT’s data teams, you will work at the heart of our research and trading operations - designing, building, and managing the data solutions that underpin our strategies. You will collaborate closely with Traders, Researchers, and Engineers to ensure our data infrastructure is robust, scalable, and able to power cutting-edge quantitative research. Your work will involve tackling complex challenges in data acquisition, engineering, and platform design.  Examples of potential roles and projects include:  * Quantitative Data Analyst  Source, design, and onboard new datasets in line with priorities set by trading desks. Support data integration into trading systems, propose data-driven solutions to unlock new research opportunities, and manage the full lifecycle of data sourcing projects, from acquisition and exploration to design. Collaborate with Data Engineers to bring datasets into production at scale.  * Data Engineer  Manage extensive datasets across QRT’s research and trading platforms. Design and implement data stores and APIs, monitor the health and performance of data processes, and deliver production-ready datasets. Provide direct support to Researchers and Traders to ensure they can fully leverage the capabilities of our data platform.  * Data Platform Engineer  Build and maintain the large-scale data infrastructure that powers our research environment. Focus on the ingestion, processing, and serving of data for both real-time and batch workflows, ensuring the platform operates with low latency and high availability. Continuously monitor and optimize system performance to handle growing volumes of complex datasets efficiently.    Your present skillset   * Strong core computer science foundations, including algorithms, data structures, parallel programming, and object-oriented programming (OOP).  * Genuine interest in software engineering, infrastructure, or data engineering within a low-latency environment, working in Python. * Interest to build expertise in high-performance, real-time trading systems  * Excellent communication and analytical skills - you will interact directly with Traders and Researchers   * Drive for rapid autonomy and the ability to work in a fast-paced, high-performance setting.  * Rigorous and structured approach to problem-solving.  Preferred qualifications (a plus):  * Knowledge of databases such as SQL or NoSQL.  * Experience in front-end development.  * Interest in financial markets and/or algorithmic trading.     Interview Process:  * Application - Submit your application online. We review applications on a rolling basis, so we recommend applying early to maximize your chances.  * Technical Assessment - Selected candidates will be invited to complete a coding challenge designed to evaluate core technical and problem-solving skills.  * Interviews - Shortlisted applicants will proceed to interviews, conducted either on-site or via Microsoft Teams. These will assess both your technical expertise and your alignment with our culture and values.       QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.

Vestiaire Collective

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 We are seeking a Foundational Machine Learning Engineer for a high-impact greenfield opportunity to build our MLOps infrastructure from the ground up at Vestiaire Collective. While driving our AI authentication initiatives (deploying multi-model approaches including computer vision for luxury product authentication and counterfeit detection) will be your immediate focus, your long-term mission will be to scale foundational architecture across the entire marketplace. You will expand our ML capabilities to power broader domains, primarily focusing on search and recommendation systems, with future expansions into dynamic pricing and marketing technologies. Acting as the bridge among Applied Science, Data Platform, and Backend Engineering, you will design robust, decoupled architectures and spearhead the MLOps strategy with our Director of Data, prioritizing system maintainability, engineering hygiene, and the reliable deployment of complex models, ensuring all our ML models across the board deliver high-throughput, low-latency business impact. What You Will Do Short-Term Impact (First 6 Months): Partner closely with the Operations squads and Data Scientists to accelerate ML and RAG prototypes into resilient, production-ready code. You will directly integrate with the team to deploy, optimize, and scale heavy-width CV and VLM models focused on fraud detection and luxury product authentication, immediately improving our trust and safety ecosystem. Mid-Term Foundation (MLOps Lifecycle & Infrastructure): Lead the end-to-end foundational groundwork of our ML lifecycle by designing robust systems for Data & Feature Management, Model Tracking & Registry, and Model Serving & Monitoring. You will scale infrastructure by automating continuous retraining pipelines that handle diverse deployment cadences (from daily fraud detection to weekly recommendations), design resilient multi-model architectures, and critically evaluate the technical overhead and TCO of our in-house tools against enterprise-grade platforms to ensure long-term resilience. Long-Term Vision (Centralizing 360-Degree MLE Capabilities): Act as a pioneer and cornerstone hire for the ML engineering discipline at Vestiaire Collective, setting the technical standards to help scale the AI/ML organization. You will transition into a centralized foundational role, moving beyond single-squad operations to mentor the team and provide horizontal ML infrastructure support to multiple domains, including Search, Discovery, Pricing, Marketing, and Data Platforms. Who You Are Must-Haves: Experience: 5-8+ years of hands-on experience in Machine Learning Engineering, specifically focused on building and scaling MLOps infrastructure and productionizing ML systems. Production Infrastructure: Proven expertise in deploying low-latency, high-throughput ML inference services (using FastAPI, TorchServe, Triton Inference Server, or Ray Serve) across both classical lightweight and heavy-width ML models (PyTorch/TensorFlow). Strong preference for AWS (EKS, EC2, SageMaker) / Snowflake and Open Source ecosystems over GCP/Azure. MLOps & Pipelines: Deep experience building automated, continuous model retraining pipelines to handle concept drift (ranging from daily to weekly cycles). You have orchestrated decoupled, multi-model AI architectures using tools like Airflow, Kubeflow, or Metaflow, and possess strong expertise in model registry and tracking tools like MLflow or Weights & Biases. Feature Stores: Hands-on experience evaluating, building, or extensively leveraging online (Redis, DynamoDB) and offline (Snowflake, S3) Feature Stores in a production environment. Familiarity with frameworks like Feast or custom dbt-based pipelines is highly valued. Strategic Builder Mindset: You are an analytical builder who thinks long-term. You can successfully evaluate TCO for bespoke internal systems versus enterprise tools, anticipate technical liabilities, and design robust architectures that handle unpredictable peak traffic surges. Collaboration & Engineering Hygiene: Strong cross-functional communication skills. You excel at translating complex ML prototypes into highly scalable production code backed by strict version control, rigorous testing, and CI/CD best practices, seamlessly connecting data science innovation with backend engineering execution. Nice-to-Haves: Relevant Domain Expertise: Background in E-commerce, Single-SKU Marketplaces, Search & Recommendation, Trust & Safety, or Counterfeit Detection. Vision, Edge & Optimization: Hands-on experience with Vector Databases, Visual RAG pipelines, deploying Deep Learning VLM models, and optimizing models for edge computing or low-latency inference (e.g., ONNX, TensorRT). Infrastructure & Observability: Advanced experience with containerization (Docker, Kubernetes), Infrastructure as Code (Terraform), and data transformation workflows (dbt). Familiarity with setting up advanced monitoring for model performance, concept drift, and system health (Datadog, Prometheus).