G

G-Research

Quantitative finance research applying rigorous methods

Elastic Security Engineer

Security Engineering & ArchitecturePosted on Nov 11
Full-timeLondon, UKNo salary listed

About the Job

Requirements

  • Strong experience with Elastic Stack and data engineering technologies such as Logstash, Beats, Kafka, or similar technologies.
  • Expertise in Elastic data modelling, indexing, and performance optimisation.
  • Experience designing scalable log collection and processing architectures.
  • Knowledge of data governance, retention, and security telemetry across cloud, endpoint, and network environments.
  • Experience with Azure, AWS, GCP, or hybrid environments.
  • Ability to explain complex concepts to technical and non-technical stakeholders.
  • Experience mentoring others and working across teams to improve security outcomes.

Responsibilities

  • Act as the technical authority and subject matter expert for security data within the Elastic Security ecosystem.
  • Design, implement, and maintain scalable security data ingestion pipelines into Elastic SIEM.
  • Own the onboarding, tuning, normalisation, and stabilisation of security data feeds across cloud, endpoint, network, and SaaS sources.
  • Define and continuously improve data quality standards to support detection, investigation, and threat hunting.
  • Contribute to the operation, performance, and evolution of the Elastic Security platform.
  • Apply software engineering and scripting skills, including Python or Bash, to automate ingestion, validation, monitoring, and operational workflows.
  • Support configuration as code and infrastructure as code to improve consistency and scalability across security tooling.
  • Drive the development of a security data strategy aligned with business and regulatory requirements.

About the Company

G-Research

Quantitative finance research applying rigorous methods

G-Research applies scientific rigour to solve complex challenges in financial markets. It brings together world-class researchers and engineers to develop data-driven models and trading ideas, testing them through rigorous experimentation and robust infrastructure. Unlike many traditional asset managers, its culture emphasizes deep exploration, reproducibility, methodical execution, and long-term thinking. The goal is to generate alpha through disciplined, evidence-based research that informs investment decisions and advances understanding of financial markets.

Company size
1,001-5,000
Company stage
Not listed
Total funding
Not listed
Headquarters
London, United Kingdom
Founded
2001

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