Analytics Engineer I - AI & Data Enablement

Requirements

• Exposure to BigQuery, dbt, Looker or similar tools.
• Experience using AI tools in more advanced ways, such as prompt iteration, code review support, data exploration, workflow automation or output evaluation.
• Understanding of APIs, data pipelines or workflow automation concepts.
• Exposure to process improvement, automation, process intelligence or operational analytics use cases.
• Previous professional, internship, academic or personal project related to data, analytics or AI.
• Interest in responsible AI, data governance, privacy and quality.
• WHAT SUCCESS LOOKS LIKE
• In your first months, success will mean:
• You understand the main data assets, tools and workflows used by the team.
• You can contribute to simple data transformations and documentation with guidance.
• You can validate outputs and raise quality concerns when something does not look right.
• You use AI-enabled tools responsibly to support your work, without blindly trusting the results.
• You start connecting business problems with reliable data solutions.
• You show curiosity, ownership and willingness to learn from feedback.
• Over time, you will grow towards owning small data products independently and contributing to AI-enabled data initiatives with increasing autonomy.
• IMPORTANT NOTE
• This is not a pure AI Engineer role.
• This is an Analytics Engineer I role within Data Enablement, with an expectation that the person will progressively develop AI competencies as part of the natural evolution of Analytics Engineering.
• We b

Additional Information

We are looking for an Analytics Engineer I to join Data Enablement, our team focused on building reliable data foundations and enabling the next generation of data products at Glovo. This is an entry-level role for someone who wants to grow in Analytics Engineering while developing strong foundations in AI-enabled data work. This is not a pure AI Engineer role, but we are looking for someone who is already AI-savvy and has practical experience using AI tools to improve their work. We expect the candidate to understand both the value and the limitations of AI-generated outputs. As part of DataX, you will help us make data more accessible, reliable and useful for business teams, while progressively contributing to initiatives involving AI-assisted workflows, data quality, documentation, automation support and GenAI-enabled data experiences. You will work closely with Data Analysts, Product Managers, Business stakeholders and other Data teams to understand problems, build trusted data assets and support the responsible use of AI in data work. You will: Support the development of reliable datasets, data models and analytical assets used by business and technical teams. Work with more senior engineers to understand business requirements and translate them into simple, well-defined data solutions. Write, test and maintain SQL transformations with a strong focus on quality, readability and documentation. Help validate data outputs and ensure they are accurate, understandable and fit for purpose. Apply your existing AI literacy to real Analytics Engineering use cases, including prompt design, context grounding, hallucination risks, human validation and responsible AI usage. Contribute to internal knowledge bases, documentation and best practices that help teams use data and AI-enabled tools safely. Collaborate with stakeholders to understand repetitive pain points, manual processes and opportunities where better data products can improve decision-making. Learn and apply engineering best practices around version control, testing, monitoring, data quality and access control. Grow into an autonomous Analytics Engineer capable of owning small data products and contributing to AI-enabled data initiatives. WHAT YOU WILL BRING TO THE RIDE 1-2 years of experience in Analytics Engineering, Data Analytics, Data Engineering, BI, Computer Science, Engineering, Mathematics, Statistics or a related field. Strong foundational knowledge of SQL, including joins, aggregations, filtering, date logic and basic query debugging. Basic understanding of data modelling concepts such as fact tables, dimensions, entities, joins, granularity and data quality. Proficiency in Python or another programming language for data work, automation or analysis. Hands-on experience with Git or similar collaborative software development practices. Above-average AI literacy: practical experience using AI or GenAI tools for coding, analysis, documentation, exploration or productivity. Clear understanding that AI-generated outputs need to be reviewed, validated and used responsibly. Interest in modern data stack tools such as BigQuery, dbt, Looker or similar technologies. Strong analytical thinking and problem-solving mindset. Ability to communicate clearly with both technical and non-technical stakeholders. High attention to detail and willingness to document your work properly. Good vibes, team player and hands-on attitude. Fluency in English.

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