Analytics Engineer in Madrid

• Flexibility Policy - Average is 30 days taken, however, we do not have a hard cap of number of vacation days as we know life happens
• Temporary work from abroad - If you're based in the EU, you can work outside your country of residence for up to 180 days per year
• All the hardware you need and your own MacBook
• If you are located in Hamburg or Berlin, you'll get a Deutschland ticket or access to a mobility budget with the NAVIT app to get you to and from the office space
• Subsidised Urban Sports Club or ClassPass membership based on location
• In case you're located in one of the cities where most of our fellow Appinioneers are (Hamburg, Berlin, Munich, London, Madrid, Barcelona, or New York), you can get access to our Co-working spaces

We are looking for an Analytics Engineer (m/f/d) with strong data engineering skills to join our Data team and help build a scalable, reliable, and well-structured data foundation across Appinio.
In this role, you will design and maintain clean, scalable data models that make data easy to access, understand, and use across the organisation. You will use SQL, dbt, and Python to transform raw data into meaningful datasets, develop automated workflows, and ensure consistency and reliability across our data warehouse.
Beyond data modelling, you will contribute to building, maintaining, and optimising data pipelines and integrations. You will work with APIs, databases, and third-party data sources to ensure reliable data ingestion, troubleshoot pipeline issues, and improve the overall stability and performance of our data infrastructure.
Working closely with Analytics, Product, Engineering, and business teams, you will help define and standardise key business metrics such as revenue, bookings, and churn, ensuring a single source of truth across the company. You will also take ownership of data quality through automated testing, validation, and monitoring across the data lifecycle.
You will explore and leverage AI-powered tools to improve development workflows, automate repetitive tasks, and enhance data engineering and analytics processes. You will also help prepare our data foundation for AI-driven analytics by ensuring that data models, business definitions, and documentation are structured, consistent, and accessible.
You will contribute to documentation, performance optimisation, and the continuous improvement of our data stack, helping us build a robust and scalable data platform that enables teams across Appinio to make faster, more informed decisions.

• Data Modeling: Design, build, and maintain clean, scalable data models in the data warehouse using SQL and dbt, making data accessible and reliable for analysts and business users.
• Data Transformation: Develop and maintain data transformation workflows, turning raw data from multiple sources into structured, analytics-ready datasets.
• Data Pipelines & Integration: Build, maintain, and troubleshoot data pipelines and integrations, ensuring reliable data ingestion from APIs, databases, and third-party systems into the data warehouse.
• Data Quality & Testing: Ensure data accuracy and reliability by implementing automated tests, validations, monitoring, and proactive issue resolution across data pipelines and models.
• Documentation: Document data models, pipelines, business definitions, and transformation logic to ensure transparency, maintainability, and knowledge sharing.
• Business Logic Ownership: Define, implement, and standardize key business metrics (e.g. revenue, bookings, churn) to ensure consistency across reporting and analytics.
• Collaboration: Work closely with analysts, business stakeholders, and engineering teams to understand data requirements and deliver reliable, scalable data solutions.
• Performance & Infrastructure Optimization: Optimize SQL queries, dbt models, and data pipelines for performance, scalability, and efficient use of data warehouse resources.

• This is a fully remote position open to candidates based in Germany, the UK, Portugal, the Netherlands, and Spain
• This role will report directly to our Team Lead, Business Intelligence