Python / Data Platform - Building Distributed Data Platforms (tooling/infra) at 25,000+ Cores, [...]

Sprint and Partners; Amsterdam; On-site; EUR 90,000 - 150,000; Full time

Benefits offered by this job

Relocation support

Conference & learning budget

Equity package

Job summary

Sprint and Partners is building a global fintech data platform with thousands of engineers, handling petabytes of storage and real-time decision-making. We hire engineers across mid, senior, and staff levels who enjoy building scalable tooling, frameworks, and infrastructure that powers data at scale.

You will work on distributed compute and storage, streaming infra, data quality tools, internal platforms, OLAP engines, and platform observability, using Python and the Hadoop ecosystem.

Qualifications

• Experience with distributed systems at scale.
• High-throughput data pipelines experience.
• Data infrastructure and platform engineering experience.
• Performance optimization and reliability engineering experience.
• Experience in private cloud or large-scale infrastructure environments.

Responsibilities

• Work on distributed compute and storage systems.
• Develop streaming infrastructure and event-driven architectures.
• Build data quality, validation, lineage, and governance tooling.
• Create internal developer platforms and self-service tooling.
• Design OLAP and analytical query engines.
• Improve platform observability, automation, and reliability.
• Use Python as the primary language.
• Leverage Kafka, Spark, Trino, Docker, and Hadoop ecosystem.

Skills

Distributed systems at scale

High-throughput data pipelines

Data infrastructure

Performance optimization

Reliability engineering

Private cloud / large-scale infra

Tools

Python

Kafka

Spark

Trino

Docker

Hadoop ecosystem

Job description

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.