Python / Data Platform Engineering - Building Distributed Data Platforms at 25,000+ Cores, 23+ [...]

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

• 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
• Python (primary language)
• Kafka
• Spark
• Trino
• Docker
• Hadoop ecosystem
• Data Contracts
• Real-time & Batch Processing

Looking for engineers who have experience with

• 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

What you get

• Highly competitive salary
• Equity package
• Permanent contract
• Relocation support
• Conference & learning budget
• 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.