We tackle the most complex problems in quantitative finance, by bringing scientific clarity to financial complexity. From our London HQ, we unite world‑class researchers and engineers in an environment that values deep exploration and methodical execution.
As part of our engineering team, you’ll shape the platforms and tools that drive high‑impact research – designing systems that scale, accelerate discovery and support innovation across the firm. The role involves designing, building and operating large‑scale distributed platforms that power our research, trading and engineering teams across on‑premises and AWS environments, using technologies such as Spark, Trino, Kafka, ClickHouse and Airflow.
Key Responsibilities
* Building, operating and scaling distributed analytics platforms across on‑premises and AWS environments
* Designing and implementing new platform features that enhance usability, scalability and the developer experience
* Collaborating with research, data and engineering teams to accelerate time‑to‑insight through modern analytics solutions
* Driving improvements in automation, observability and resilience across analytics services
* Evaluating and adopting emerging technologies such as AI assistants, data mesh and cloud‑native analytics solutions
* Defining SLAs, KPIs and monitoring strategies to ensure reliability, security and service excellence
* Participating in the out‑of‑hours rota to support critical systems
Core Skills and Technologies
* Experience running distributed data and analytics systems at scale using tools such as Spark, Kafka, Trino or Airflow
* Strong Linux skills and proficiency in Python for automation and integration
* Familiarity with infrastructure as code, using Terraform or Ansible
* Deep understanding of AWS analytics technologies including EMR, MSK, Athena, Redshift, Glue and MWAA
* Experience with CI/CD and observability tools such as Jenkins, ArgoCD, Prometheus, Grafana and OpenTelemetry
* Strong problem‑solving skills and a systematic approach to diagnosing and resolving issues
Highly Desirable Skills
* Experience with streaming frameworks such as Flink, Kafka Streams and Kafka Connect
* Knowledge of modern data lake technologies including Delta Lake, Iceberg and Glue Data Catalog
* Exposure to DataOps practices and collaboration with Data Engineering teams
* Familiarity with GPU‑accelerated analytics using Spark with GPUs or RAPIDS
* Programming experience with Java, Scala, C#, Python or Go
Benefits
* Highly competitive compensation plus annual discretionary bonus
* Lunch provided (via Just Eat for Business) and dedicated barista bar
* 35 days’ annual leave
* 9% company pension contributions
* Informal dress code and excellent work/life balance
* Comprehensive healthcare and life assurance
* Cycle‑to‑work scheme
* Monthly company events
Diversity and Inclusion
G‑Research is committed to cultivating and preserving an inclusive work environment. We want to ensure that applicants receive a recruitment experience that enables them to perform at their best. If you have a disability or special need that requires accommodation please let us know in the relevant section.
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