Job Opportunities API

About us Satispay began by rethinking the simple act of a payment to remove the friction from our daily routines. But we didn’t stop there. Today, we are building a complete financial platform designed to empower people and concretely improve their lives. By giving our 6 million users a clear, open path to pay, save, and invest, we are evolving into the definitive destination for every financial need. What you'll be doing As our Senior Data Engineer, you won't just maintain pipelines; you will shape the evolution of our next-generation data platform. You will architect, scale, and govern an ecosystem that empowers Data Analysts and business teams to autonomously model, analyse, and leverage data safely. Here's what your day-to-day will look like: - 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. You will own the core dbt framework, establishing best practices, CI/CD pipelines, and robust testing frameworks to make analytics engineering scalable and decentralised. - 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. You will manage our Feature Store, build robust data pipelines for model training and inference, and provision collaborative analytics environments to empower Data Scientists and Analysts. - 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, moving the platform beyond batch. - AI Tooling & Agentic Engineering - Drive team-wide productivity gains by exploring, integrating, and maintaining AI-assisted development workflows. This includes leveraging tools like Claude Code and deploying AI agents to automate data engineering tasks, optimise pipeline monitoring, and accelerate delivery. - 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 – ingestion streams, transformations, feature store, and Redshift Serverless. Own 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 while maintaining trust. - Mentorship & Tech Leadership - Act as a technical mentor for junior/mid-level engineers, champion documentation, and establish engineering standards that elevate the entire team’s capabilities. Who we're looking for We need a problem-solver who loves teamwork and gets things done. If you're curious and ready for real ownership, you'll fit in! Does this sound like you? - Relevant education and technical background - You have a 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 - You are highly skilled in SQL for data manipulation and analysis and have practical experience with at least one of the following: Python, Java, or Scala for building robust data pipelines. - Architectural Knowledge - You are familiar with both batch and real-time streaming data architectures and understand the principles behind designing scalable and resilient data systems. -