This is a [Wrocław]-based hybrid opportunity – [3] from home per weekAs an [Data Platform Engineer] you will be working for our client – [a global quantitative & systematic investment manager using a scientific, tech-driven approach across all liquid asset classes]. You will be responsible for [building the tools, pipelines and observability that power data analysis, monitoring and system automation for low-latency trading at scale].Your main responsibilities: Design & implement systems to collect, process and manage large-scale market and internal datasets Build and maintain performant, reliable data pipelines/services (Python, Airflow, AWS/S3) Develop monitoring, alerting and observability for data platforms (Grafana/Prometheus/ELK) Integrate data delivery and compute workflows into production with engineers & researchers Improve CI/CD and infrastructure-as-code to streamline deployments and operations Evolve expertise in distributed computing, storage architectures and performance engineering You’re ideal for the role if you have: 3–7 years’ experience with strong Python and shell scripting Solid grasp of data engineering, ETL and time-series data (SQL/Pandas) Hands-on Linux, networking and performance troubleshooting skills Familiarity with containers & orchestration; exposure to Airflow and distributed compute Experience with observability/metrics stacks (Grafana, ELK, Prometheus) Exposure to CI/CD and IaC; bonus: AWS at scale (e.g., ECS/EKS) and ClickHouse Ownership mindset, problem-solving drive and eagerness to work close to trading