Python / Data Platform Engineering - Building Distributed Data Platforms at 25,000+ Cores, 23+ PB Storage & 10,000s of Daily Jobs - VISA/Relocation - Amsterdam, Netherlands
Build the global platform that powers thousands of data workloads, close to a thousand engineers, petabytes of storage, and real-time decision-making.
We're hiring engineers (mid, senior and/or staff level) who thrive in large-scale distributed systems and enjoy building the tooling, frameworks, and infrastructure that powers data (the heart) of this global fintech (unicorn). Platform/Data Engineering is done with a SWE mindset and practices - You don't think tools first, you think about creating solutions that scale globally, today and in the future first and then apply the right tool for the problem at hand or create it yourself.
You'll work on
- Large-scale data platform engineering
- Distributed compute and storage systems
- Streaming infrastructure and event-driven architectures
- Data quality, validation, lineage, and governance tooling
- Internal developer platforms and self-service tooling
- OLAP and analytical query engines
- Platform observability, automation, and reliability
Tech Stack
- Python (primary language)
- Kafka
- Spark
- Trino
- ClickHouse / Druid / Pinot
- Kubernetes
- Docker
- Hadoop ecosystem
- Data Contracts
- Real-time & Batch Processing
Looking for engineers who have experience with
- Building internal platforms, frameworks, or developer tooling
- Distributed systems at scale
- High-throughput data pipelines
- Data infrastructure and platform engineering
- Performance optimization and reliability engineering
- Private cloud or large-scale infrastructure environments
Nice to have
- Java, Go, or Rust
- Experience with OLAP architectures
- Experience supporting data science or ML platforms
- Strong systems engineering mindset
What you get
- Highly competitive salary
- Equity package
- Permanent contract
- Relocation support
- Visa sponsorship
- Conference & learning budget
- Hybrid working model
- Work on genuinely large-scale technical challenges
If terms like Spark scheduler bottlenecks, Kafka throughput, distributed query planning, metadata-driven platforms, platform engineering, or developer experience for data teams excite you, I'd love to speak with you.
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