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Quant Capital

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Cloud Data Engineer – Quanttrading

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Summary

Design and operate AWS/hybrid-cloud data infrastructure for a quantitative trading firm: build infrastructure-as-code repositories, automate CI/CD pipelines, and standardize data management to enable scalable analytics. Core stack includes AWS data services, Terraform/CloudFormation, Kubernetes, and Python or Go.

Overview

In this role, you will design and operate cloud data infrastructure to support quantitative engineering and research. You will work with cross-functional teams to standardize data management and enable scalable analytics in a hybrid cloud. You will build and maintain an infrastructure-for-code repository and promote secure, efficient data pipelines. This opportunity sits at a well-funded fintech startup-like firm in London, offering high-caliber colleagues and impactful, technology-driven projects.

Pay / Benefits
  • benefits
  • bonus
Responsibilities
  • Oversee multiple AWS environments and foster cross-team collaboration
  • Integrate services and access paths within a data-intensive hybrid cloud
  • Develop, enhance, and automate CI/CD pipelines
  • Gather requirements to consolidate data management under a unified structure
  • Propose hybrid cloud data engineering solutions and provide examples
  • Stay current with cloud technology, services, and networking advancements
  • Evaluate vendor solutions and deliver internal training to clients
Key requirements
  • Bachelor’s degree in a STEM field
  • 5+ years of AWS experience, with broad exposure to other cloud vendors
  • Deep knowledge of AWS data management and pipelining tools (VPC, EMR, S3, RDS, Kinesis, Glue, Redshift, Data Pipeline)
  • Proficiency with infrastructure-as-code tools (Terraform, CloudFormation)
  • Strong Kubernetes knowledge
  • Proficiency in Python or Go
  • Experience building highly available, fault-tolerant big data environments (Hadoop, Spark, PostgreSQL) and using EMR, Athena, RDS
  • Security-first mindset with IAM and KMS usage
  • Working knowledge of cloud data analytics tools like Databricks and Snowflake
  • strong collaboration and communication
  • ability to translate business and technical requirements
  • training and knowledge sharing
  • Terraform
  • CloudFormation
  • Kubernetes

Skills

See also

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