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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, with a focus on solving real user problems and maintaining high operational standards.
As FinCrime Analytics Engineer, you will drive the data strategy to elevate risk decision-making across the organization. Your responsibilities include:
**Core 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 collection of high-quality data 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 key 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 and executing across high volumes of data.
- Simplify complex data models into clear, efficient data relations that can be easily queried with SQL to improve overall performance and ensure high data quality.
You will work in a fast-paced environment where evolution is constant. The role offers hybrid flexibility with three days per week in-office (Tuesday, Thursday, plus one day of your choice), with options to request additional remote time. Benefits include private health insurance, psychological support, stock options, meal vouchers, relocation support, professional development programs, language courses, unlimited PTO, and enhanced parental leave.
**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.
