Senior Analytics Engineer

Job description

About seQura seQura provides innovative, flexible and easy-to-use payment technologies that help merchants acquire, convert and retain more customers. We make a difference in sales performance by tailoring our solutions to different sectors, to address their unique pain points and deliver superior results in Retail, Education, Eyewear, Repairs and Travel. We also empower smart shopping to consumers who seek more value, convenience, and flexibility in their shopping, with new payment experiences that allow them to save, access interest-free credit, or pay in small, comfortable installments of up to 24 months. Born in Barcelona, seQura is a privately-owned fintech, currently expanding throughout southern Europe and Latin America, growing above 50% CAGR. Over 6000 businesses, almost 3 million shoppers, and almost 400 employees continue to rate us as one of the most loved and trusted fintechs out there, with an NPS of 87%, a Trustpilot rating of 4.7/5, and a Glassdoor rating of 4.1/5. About the role 🤓 We are looking for a Senior Analytics Engineer to own the analytics enablement layer across seQura's core business domains — building the trusted, well-governed data foundation that business, product, and data science teams rely on to make decisions and ship models. This role sits at the intersection of data modeling, business domain understanding, governance, and semantic precision. You will design and deliver production-grade dbt models, shape the semantic layer to be AI-ready, and set the quality bar for the analytics enablement team. You will work closely with business circle leads, Data Scientists, across Risk, Finance, Payments, and Collections, and the Platform and Data Engineering team. What challenges you'll be solving 🚀 Championing a single source of truth for core business metrics — unifying definitions, socializing, and governing the canonical KPIs used across seQura, so that whether a metric appears in a dashboard, a DS model, or an executive report, it means the same thing. Building domain data products that serve both operational reporting and ML feature needs, ensuring downstream teams can self-serve reliably. Designing, building, and shipping production-grade dbt models across multiple business domains — Payments, Debt Collection, Risk, and Finance — writing clean, well-tested, and well-documented SQL. Owning the governance layer: data contracts…