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Satispay is a fintech platform serving 6.5M+ users, offering payments, benefits, investing, and cards. The company is building what it aims to be the most loved financial platform in the world by solving real user problems with accessible financial services.
As FinCrime Analytics Engineer, you will drive the data strategy to elevate risk decision-making by ensuring analytical data is readily available, well-structured, and aligned with operational needs.
Key responsibilities:
- Design and develop the Data Mesh layer by creating robust data models and building pipelines that integrate into the company's federated data strategy, ensuring scalable and governed data ownership.
- Ensure high-quality data collection and manage high-volume batch processing to maintain optimal performance for dashboards and analytical models.
- Design and implement AI-powered agentic workflows for data transformation and exploration, accelerating analytical capabilities and enabling self-service for stakeholders.
- Independently direct cross-functional initiatives across diverse business requirements, translating technical insights into impactful actions and data model optimizations.
- Develop high-value features for the feature store using optimized code capable of processing high volumes of data.
- Simplify complex data models into clear, efficient data relations that can be easily queried with SQL to improve performance and ensure data quality.
You will work in a hybrid model with three days per week in-office (Tuesday, Thursday, plus one day of your choice), with flexibility to request additional remote time. The role requires relocation to the Barcelona office.
Requirements:
- 5+ years of experience in data engineering or analytics engineering.
- High proficiency in SQL, Python, Big Data, distributed processing, dbt, Airflow, Spark, or similar technologies.
- Hands-on experience building ETL data pipelines.
- Excellent communication skills and fluency in English.
- Nice to have: Experience with Feature Stores, Graph Databases, and Machine Learning Engineering.
