About QRT
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.
Responsibilities
•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.
Requirements
•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.
Nice to have
•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.
What we offer
In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.
