Summary: Join the Quick Commerce team as a Data Engineer to design, build, and deploy ML models for content creation and smarter product assortment, with an interest in transitioning to an MLE role. Highlights: 1. Opportunity to redefine quick commerce through AI and machine learning 2. Work on building an AI engine for vendor catalog management 3. Transition potential to an MLE role in the future Barcelona, Spain Full\-time Tech **Who we are** -------------- As part of the Delivery Hero Group, Glovo is the fastest\-growing multi\-category app in Spain, connecting millions of users with businesses, and couriers, offering on\-demand services from more than 170,000 local restaurants, grocers and supermarkets, and high street retail stores, operating in more than 1500 cities across 25 countries. We are proud to make a significant and valuable contribution to the strategic positioning of the Delivery Hero Group on the global market through our unique expertise. ### **Job Description** **About the opportunity** We are on the lookout for a **Data Engineer** to join our Quick Commerce team on our journey to always deliver amazing experiences. Be part of redefining how customers experience quick commerce. You’ll help build technology that scales our non\-food offerings, reaching new market segments and driving revenue growth. By innovating within our Quick Commerce Team, you’ll make Delivery Hero the go\-to platform for a broad range of products, helping us grow faster and deliver more value to customers around the world. We are building the AI engine that helps vendors upload and update their catalog on our platform in the easiest and quickest way possible. As a Data Engineer, you will work side\-by\-side with a Data Scientist and Software Engineers to design, build, and deploy ML models that automate content creation and power a smarter product assortment. We need a Data Engineer, but someone who is interested in transitioning to an MLE role in the future, once the product is more mature. This means this is a role for someone who has a strong background in applied ML, and is hungry to build a reliable and scalable image recognition system. If you’re a strong coder with a problem\-solving mentality and thrive in high\-scale applied ML problems, we’d love to talk. **THE JOURNEY** * Design and build resilient ETL/ELT pipelines to ingest, clean, and validate image data and associated metadata (e.g., vendor catalog features). * Work with the Data Scientist to create and maintain the "Ground Truth" dataset (human\-validated, accurately labeled data) necessary for calibrating the third\-party model and benchmarking the accuracy. * Build reliable, production\-grade feature pipelines to ensure that data used during model training is identical to the data used during live inference (avoiding training/serving skew). * Build the automated infrastructure to support the Data Scientist's model training cycles, focusing on data versioning and reproducibility. * Contribute to the long\-term vision of products image recognition, while delivering short\-term wins that unlock measurable business impact. ### **Qualifications** **WHAT YOU WILL BRING TO THE RIDE** * Bachelor's degree in computer science, information systems, mathematics, statistics, or a related field. * 3\+ years of professional experience as a Data Engineer or Machine Learning Engineer. * Advanced knowledge of SQL and distributed processing frameworks. * Excellent engineering skills: write clean, maintainable Python code. * Strong systems\-level problem\-solving skills, with the ability to balance performance, scalability, maintainability, and business impact. * Excellent communication skills, with the ability to clearly articulate complex technical problems and solutions to both engineers and business stakeholders. **Nice to have:** * PhD in AI, Machine Learning, or a related field. * Experience on Computer Vision (CV). * Excellent engineering skills: Experience bringing ML models into production with best practices for observability, monitoring, and performance. * Experience with CI/CD pipelines and commonly used tools in ML Engineering such as Metaflow, MLflow, Airflow, Grafana, and similar. * Experience with large\-scale ML model validation, experimentation, and A/B testing. * Hands\-on experience with major cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).