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Adyen is a global financial technology platform providing payments, data, and financial products for enterprise customers including Meta, Uber, H&M, and Microsoft. This role sits at the intersection of data engineering, backend engineering, and systems engineering, focusing on building foundational tooling and infrastructure for Adyen's on-premise big data platforms.
You will work on scaling and optimizing real-time analytical systems (Druid, FastAPI, Java, Kafka) or designing data contracts, validation frameworks, and robust Kafka ingestion pipelines, depending on your expertise. The platforms you'll support handle 10s of products, 100s of developers, and 1000s of daily jobs. The largest platform operates 500+ nodes with 25k+ CPU cores, 240+ TBs of RAM, and 23+ PBs of storage, running tens of thousands of Spark jobs daily.
Key responsibilities include developing and maintaining high-performance distributed big data platforms, working with various databases, filesystems, and compute engines, building tools for metadata collection and data lineage, optimizing CI/CD and infrastructure within a large-scale private cloud, and developing stream processing applications to reduce time-to-insight for analysts and data scientists.
You'll collaborate closely with data engineers, ML scientists and engineers, and DataOps Engineers to build and roll out these tools. The role requires balancing innovative new tool development with relentless improvement of existing infrastructure.
Required qualifications: 6+ years of experience with Python or Java and object-oriented programming; proven experience developing and maintaining distributed compute/data systems (Spark, Trino, Druid, ClickHouse); experience with real-time and batch pipelines (Kafka, Spark Streaming); strong communication skills and ability to bridge technical teams including analysts, data scientists, infrastructure, and security teams.
