Analytics Engineer - Fintech

Analytics Engineer

Location: Madrid (Hybrid: 4 days office / 1 day WFH)

We are looking for an Analytics Engineer to be embedded within one of our core product domains. You will be the owner of your domain's data warehouse, responsible for setting the standards, patterns, and practices that govern how data is modelled, structured, and consumed within your domain.
You will build the semantic layer that provides the context, definitions, and structure necessary for AI agents and business stakeholders to leverage data effectively — turning raw domain data into trusted, reusable assets that the wider organisation can depend on.

We operate under a Domain-Driven Design (DDD) approach, where Product, Engineering, and Data align on shared goals to eliminate bottlenecks and ensure architectural coherence across the organisation.

What you will do:

• Partner with the business to gather requirements and define technical action plans for robust data products.
• Lead projects from initial definition to delivery, including strategies for stakeholder (impact and testing)
• Ability to split between strategic management and technical execution; you are equally comfortable architecting a multi-domain roadmap as you are diving into the IDE to ship a critical fix
• Proactively monitor and govern data products to ensure high reliability. You will design and propose ongoing alerting frameworks to catch anomalies before they reach the business.
• Deliver hands-on data models using modern software engineering practices (version control, testing, and CI/CD).
• Collaborate with team members to reinforce best practices across the platform, encouraging a shared commitment to quality.
• Manage overall pipeline orchestration using Airflow (hosted in Cloud Composer).
• Contribute to and promote best practices for query development, version control, code review, documentation, and testing

What we’re looking for:

• 2+ years experience in similar roles, solid analytics engineering background — dbt, SQL, data modeling, and a strong sense of what makes a data product trustworthy and maintainable.
• High proficiency in SQL with the ability to build, robustly test, and optimize complex models.
• A strong sense of what makes a data product trustworthy, maintainable, and valuable to the end user.
• Finance domain knowledge, you understand how financial data works: P&L, FX, exposure, reconciliation.
• Experience with (or a genuine curiosity for) Domain-Driven Design and how it shapes team ownership and data structure.
• Fluency in English (Spanish is a plus).