Data engineer in quantitative trading

Qube Research & Technologies; Amsterdam; On-site; EUR 90,000 - 130,000; Full time

Benefits offered by this job

Гибкий график

Обучение и развитие

Job summary

Qube Research & Technologies ищет инженера по данным в Амстердаме для проектирования, сборки и эксплуатации высокопроизводительных распределённых data-платформ и пайплайнов на Python, поддерживающих исследовательские и торговые workflows. В обязанности входит создание production-grade сервисов, обработка больших объёмов рыночных данных и обеспечение их надёжности.

Координаторская роль требует опыта в Airflow, AWS и современных фреймворках обработки данных; дружелюбное сотрудничество с командами

Qualifications

• 5+ лет опыта в production-grade data platforms и/или крупных системах обработки данных.

Responsibilities

• Проектирование и внедрение высокопроизводительных распределённых систем обработки данных.
• Разработка production-grade Python сервисов и пайплайнов для сбора, обработки и распространения больших объёмов данных.
• Обеспечение наблюдаемости, качества данных и устойчивости цепочек обработки.
• Сотрудничество с исследователями, трейдинг- и инженерными командами, внедрение новых датасетов и инфраструктурных улучшений.
• Ответственность за архитектуру, внедрение и эксплуатацию систем.

Skills

Python

Distributed systems

Airflow

AWS

Data processing

Tools

Apache Parquet

Apache Arrow

Spark

Ray

Dask

Docker

Kubernetes

Terraform

CloudFormation

Job description

Описание:

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

Задачи:

Design, build, and operate high-throughput distributed data systems that power quantitative research and production trading workflows. Develop production-grade Python services and pipelines to acquire, process, validate, and distribute large volumes of market and reference data. Build systems supporting both large-scale historical data processing and time-critical overnight pipelines on the path to trading. Design scalable processing architectures that partition work across QRT’s on-premises compute infrastructure and AWS. Build comprehensive observability and operational tooling to monitor data freshness, completeness, quality, and pipeline health. Collaborate closely with quantitative researchers, trading teams, software engineers, and infrastructure teams to onboard new datasets and improve research productivity. Take ownership of systems end-to-end, from architecture and implementation through deployment, production operation, and incident resolution.

Требования:

5+ years of experience building and operating production-grade data platforms, distributed systems, or large-scale data-processing applications. Strong Python software engineering experience, including the design of maintainable, tested, and performance-conscious production applications. Strong understanding of distributed processing concepts such as partitioning, parallelism, concurrency, fault tolerance, idempotency, and failure recovery. Experience designing systems that process large data volumes with demanding throughput or completion-time requirements. Hands-on experience with workflow orchestration technologies such as Apache Airflow. Experience with AWS services including S3, EC2, AWS Batch, IAM, CloudWatch, and cloud SDKs. Experience operating business-critical pipelines and diagnosing data, performance, and reliability issues in production. Excellent problem-solving skills and the ability to collaborate effectively across research, trading, engineering, and infrastructure teams.

Будет плюсом:

• Experience building market-data platforms or other high-throughput financial data systems
• Knowledge of non-equity asset classes such as futures, fixed income, commodities, or FX
• Experience with time-sensitive overnight processing or systems operating on the path to production trading
• Familiarity with Apache Parquet, Apache Arrow, or distributed processing frameworks such as Spark, Ray, or Dask
• Experience with Docker, Kubernetes, and Infrastructure-as-Code tools such as Terraform or CloudFormation
• C++ development experience
• Experience working in high-performance computing or hybrid cloud environments

Условия:

In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.