Senior Data Engineer

About us
Traditional banking has never really given you control over your financial life. Satispay exists to change that: to empower people through finance .
That means making financial services easy and accessible for over 6.5M users. We started with payments, then benefits, investing, cards... and we’re not done. Not even close. We follow the same logic every time: find a real problem, solve it properly, then look for the next one. The destination? To build the most loved financial platform in the world.
If you own outcomes rather than tasks, stay close to what users need, and refuse to wait for someone else to fix what's broken — you'll find people here who work exactly in the same way. And together, you'll build something worth being proud of.

What you'll be doing
As our Senior Data Engineer, you will shape the evolution of our next-generation data platform, architecting, scaling, and governing an ecosystem that empowers Data Analysts and business teams to autonomously model, analyse, and leverage data safely.
Here's what that looks like in practice:
Data Platform Architecture & Evolution - Lead the design and implementation of our modern Data Lakehouse strategy, ensuring seamless integration with Amazon Redshift & Redshift Serverless for high-performance analytics.

Self-Service Enablement & Analytics Engineering - Empower Data Analysts to contribute directly to data modelling, owning the core dbt framework, establishing best practices, CI/CD pipelines, and robust testing frameworks.

Orchestration & Workflow Automation - Build, scale, and maintain reliable orchestration layers using Apache Airflow, optimising complex DAGs for both batch and near-real-time workloads.

ML Platform Ownership & Feature Store Management - Own, architect, and scale Satispay’s Machine Learning Platform, managing our Feature Store, building robust data pipelines for model training/inference, and provisioning collaborative analytics environments.

Streaming & Real-Time Data - Design and operate streaming transformation pipelines (e.g. Apache Flink, Spark Streaming) that power near-real-time analytics and feed the feature store.

AI Tooling & Agentic Engineering - Drive team-wide productivity gains by exploring, integrating, and maintaining AI-assisted development workflows (e.g., leveraging tools like Claude Code and deploying AI agents).

Ingestion & Integration - Supervise managed ingestion workflows (Fivetran) and custom Python pipelines, ensuring cost-efficient, resilient, and reliable data flow across systems.

Performance, Cost & Reliability - Design for performance and cost across the entire platform, owning data quality, observability, and the SLAs your consumers depend on.

Data Governance, Security & Compliance - Design robust data security, access control, and privacy measures across all data assets to ensure strict compliance with financial regulations.

Mentorship & Tech Leadership - Act as a technical mentor for junior/mid-level engineers, champion documentation, and establish engineering standards that elevate team capabilities.

Who we're looking for
This role needs someone who is a problem-solver, loves teamwork, and gets things done with high curiosity and readiness for real ownership.
Relevant education and technical background - Degree in Computer Science, Engineering, or a related field, backed by 5+ years of hands-on experience designing, scaling, and maintaining complex enterprise data platform architectures.

Programming Expertise - Highly skilled in SQL for data manipulation and analysis, with practical experience in Python, Java, or Scala for building robust data pipelines.

Architectural Knowledge - Familiarity with both batch and real-time streaming data architectures and principles for designing scalable and resilient systems.

Data-Oriented Mindset - Strong passion for deeply understanding data, its meaning, the importance of correct preparation, and methodologies for quality and consistency.