> Markdown version of [/jobs/ext/1626299-analytics-engineer](https://www.wearedevelopers.com/jobs/ext/1626299-analytics-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Analytics Engineer - **Company:** Qualifyze - **Location:** Barcelona, Spain - **Salary:** €40,000.0 - €50,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Business Logic, Data Transformation, Data Structures, Database Queries, Python (Programming Language), Raw Data, Large Language Models, Multi-Agent Systems, Data Layers, Data Analytics, Looker Analytics - **Published:** July 17, 2026 - **Apply:** https://www.jobleads.com/es/job/ef96ea9dd12a4dab7dd72142e80aa86cc ## About the Role * Strong SQL skills and solid Python proficiency for data transformation, validation, and analytics workflows. * Proven experience building and maintaining dbt models and metrics, and Marts and Gold/last-mile layers. * Solid understanding of semantic layer concepts and BI consistency practices, ideally with hands-on experience in Looker or similar tools. * A strong business logic mindset, able to understand what a metric means, not just how it is calculated, and to spot when something doesn't add up. * Interest in or experience with agentic AI systems, including LLM-powered workflows and multi-agent architectures applied to analytics. * Excellent collaboration and stakeholder communication skills, with the ability to work effectively across business and technical teams. * A proactive, detail-oriented mindset with a passion for data quality, consistency, and continuous improvement. ## Description As an Analytics Engineer, you will be at the forefront of scaling our analytics capabilities through robust data modeling, metric consistency, and AI-powered workflows. Your goal is to bridge the gap between raw data and business insight, collaborating with stakeholders to understand their needs, translating them into reliable data structures, and ensuring that the metrics powering our decisions and Investor Reporting are accurate, consistent, and trustworthy. You combine a deep understanding of business logic with the technical capability to build and maintain scalable internal data models and semantic layers., * Map, document, and evaluate current data models, metrics, and reporting flows. Critically evaluate existing definitions and assumptions, identify inconsistencies, and propose improvements that enable scalability and self-service analytics. * Build and maintain metrics for Investor Reporting using dbt metrics, ensuring they are accurate, well-documented, and aligned with agreed business definitions. * Support the modeling and maintenance of Marts and the last-mile Gold layer, contributing to a robust and scalable semantic layer that serves both operational and analytical use cases. * Ensure consistency between metrics in Investor Reporting and in Looker, performing regular cross-checks and validations to guarantee a single source of truth. * Perform data sanity checks from a business logic perspective to validate new data structures before they reach production, catching issues early and reducing downstream errors. * Conduct ad-hoc analyses to support decision-making, unblock stakeholders, and surface actionable insights when needed. * Help define and document business definitions, KPIs, and data processes to ensure a shared understanding across business and technical teams. * Participate in projects involving agentic technologies applied to BI, including multi-agent implementations built with Claude and similar frameworks, innovate and expand what agentic analytics can deliver. ## Related Videos - [Why Your AI Agent Keeps Hallucinating Your Data: Building Deterministic Context Layers](https://www.wearedevelopers.com/videos/2055-why-your-ai-agent-keeps-hallucinating-your-data-building-deterministic-context-layers) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) - [The Algorithm That Nearly Killed Me: When Testing Isn't Enough](https://www.wearedevelopers.com/videos/2110-the-algorithm-that-nearly-killed-me-when-testing-isn-t-enough) - [Bringing Clarity to Event Streams: Enabling Analytics and AI Through Rich Metadata](https://www.wearedevelopers.com/videos/1616-bringing-clarity-to-event-streams-enabling-analytics-and-ai-through-rich-metadata) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) ## Related Articles - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk)