IT & Software

Analytics Services Platform Engineer

G Research

London · Greater London · United Kingdom

Overview

In this Platform Engineer role, you will design, build, and operate large-scale distributed analytics platforms across on-premises and AWS to power research, trading, and engineering teams. You will deliver high-availability, secure analytics services while improving usability and developer experience. You’ll collaborate with researchers and engineers to accelerate time-to-insight and explore emerging technologies. This is a hands-on, cross-functional role with a clear impact on platform scalability and reliability.

Pay / Benefits
  • Highly competitive compensation
  • annual discretionary bonus
  • Lunch provided via Just Eat for Business
  • 35 days’ annual leave
  • 9% company pension contributions
  • Comprehensive healthcare and life assurance
Responsibilities
  • Build, operate and scale distributed analytics platforms across on-premises and AWS
  • Design and implement new platform features to improve usability, scalability and developer experience
  • Collaborate with research, data and engineering teams to accelerate time-to-insight
  • Drive automation, observability and resilience across analytics services
  • Evaluate and adopt emerging technologies (e.g., AI assistants, data mesh, cloud-native analytics)
  • Define SLAs, KPIs and monitoring strategies for reliability and security
  • Participate in out-of-hours rota to support critical systems
Key requirements
  • Experience running distributed data and analytics systems at scale (Spark, Kafka, Trino, Airflow)
  • Strong Linux skills and Python for automation
  • Familiarity with Terraform or Ansible (infrastructure as code)
  • Experience with AWS analytics tech (EMR, MSK, Athena, Redshift, Glue, MWAA)
  • CI/CD and observability tools (Jenkins, ArgoCD, Prometheus, Grafana, OpenTelemetry)
  • Strong problem-solving and systematic diagnostic abilities
  • Strong collaboration with researchers and engineers
  • Problem-solving orientation
  • Structured and proactive approach to reliability and performance
  • Spark
  • Kafka
  • Trino

Reference: WJ-747_30154306

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