Job Ref ID:  JR0086646 YOUR MISSION Business teams at Glovo need data they can trust to make decisions — and that trust doesn't happen by accident. As part of the Marketplace Experience (MX) Data team, your mission over the next 12–18 months is to help build it: solid SQL foundations, well-documented data models, and validated outputs, while growing into someone who can own a data product from start to finish. THE JOURNEY  Support the development of reliable datasets, data models and analytical assets used by business and technical teams. Work with senior Analytics Engineers to translate business requirements into simple, well-defined data solutions. Write, test and maintain SQL transformations with a strong focus on quality, readability and documentation. Validate data outputs and ensure they are accurate, understandable and fit for purpose. Use AI tools where relevant to support your day-to-day work, always validating outputs before relying on them. Contribute to internal knowledge bases, documentation and best practices that help teams use data effectively. Collaborate with Business stakeholders, Product Managers and other Data teams to understand pain points and turn them into better data products. Grow into an autonomous Analytics Engineer capable of owning small data products end to end.   WHAT YOU WILL BRING TO THE RIDE Must haves: 1–2 years of hands-on experience in Analytics Engineering, Data Analytics, BI, Data Engineering or a similar technical data role. Strong SQL foundations, with the ability to independently write, debug and optimise analytical queries. Solid understanding of data modelling fundamentals (joins, granularity, fact/dimension tables, entities, data quality, downstream consumption). Practical experience using AI tools (ChatGPT, Copilot, Gemini or similar), understanding where they add value, where they fail, and how to validate their outputs. Strong analytical thinking and problem-solving mindset, with the ability to break down ambiguous problems into clear steps. Fluent English, with the ability to explain technical topics clearly to both technical and non-technical stakeholders. Nice to haves: Proficiency in Python or another programming language, and hands-on experience with Git and collaborative engineering practices. Exposure to modern data stack tools (BigQuery, dbt, Looker) and understanding of APIs, data pipelines or workflow automation concepts. Interest in AI-enabled data products, responsible AI, data governance or the evolution of Analytics Engineering. Relevant internship, bootcamp, academic or personal project, or previous experience in a fast-paced product/tech/marketplace environment.