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About the Role

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager. Our internships offer meaningful experience, allowing you to join a team, contribute to real projects, and learn from experienced professionals. Strong performance may lead to consideration for a full-time graduate role.

Responsibilities

Build, deploy and maintain scalable, secure pipelines using AWS S3, ECS and Lambda; centralize Workday and Greenhouse data in warehouses for BI tools.
Build Python and Kotlin services to automate workflows; test, monitor and troubleshoot data flows for accuracy.
Partner with HR, analytics engineers and leadership to align technical capabilities with business needs and deliver actionable insights.
Develop interactive BI dashboards using tools such as Python Dash.
Document pipelines, models, transformations and data assets, including ETL documentation and metric glossaries; uphold data security, compliance and access controls.
Build full-stack applications and integrations connecting core HR systems to the wider business, from conception to production.
Translate HR requirements into scalable, cloud-native tools with clean code and sound data models.
Automate repetitive work to improve employee experience and free HR colleagues for judgement, creativity, and human conversations.
Build internal tools beyond standard platform configuration, with interfaces using React, Python Dash or similar, and secure APIs and middleware connecting HR and corporate systems.
Synchronise employee data using AWS S3, ECS and Lambda; automate manual processes in Python and Kotlin to reduce overhead and data entry errors.
Test automation and implement error handling, logging and monitoring so integrations are reliable and trusted without manual checks.
Translate HR logic into technical specifications, document tools and meet GDPR and financial services security standards.
Work with functional analysts and stakeholders to identify friction in the employee lifecycle and propose software solutions.

Requirements

Penultimate or final-year students completing a bachelor’s or master’s degree.
A degree in a numerate, scientific or computing discipline; equivalent practical experience is welcome for Data Analytics.
A solid grounding in Python for automation, data or applications; object-oriented design for Software Engineering.
Data modelling: metrics and semantic layers for BI in Data Analytics; SQL and models for transactional applications and analytical reporting in Software Engineering.
Cloud familiarity, ideally AWS: willingness to learn storage and pipelines for Data Analytics; hands-on application deployment and operation, ideally with S3, Lambda or ECS, for Software Engineering.
An analytical approach, clear communication, and enthusiasm for collaborating with technical and non-technical colleagues.
Exposure to ETL concepts or tools through study, internships or personal projects.
Experience with Kotlin or another statically typed JVM language.
Front-end experience with JavaScript, HTML and CSS; familiarity with React, Bootstrap or Python Dash.
Understanding of REST and SOAP APIs, JSON, XML and data transformation.
Exposure to Workday, Greenhouse, employee lifecycle data or HR metrics such as retention, engagement and performance.
Infrastructure as code, particularly Terraform.
Experience integrating AI, LLMs or agents, understanding their strengths and limitations.
Front-end or user-experience skills, including Python Dash or React.
Git and CI/CD pipelines.

Skills

AWS S3
AWS ECS
AWS Lambda
Python
Kotlin
Python Dash
React
Bootstrap
JavaScript
HTML
CSS
REST APIs
SOAP APIs
JSON
XML
Terraform
Git
CI/CD pipelines

Location

London

Work Type

Internship
Final-year internship
Penultimate-year internship

Experience Level

Internship

Education Level

Bachelor's degree
Master's degree

Benefits

Initiatives and programs to enable employees achieve a healthy work-life balance.

About the Company

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager operating across liquid asset classes and markets worldwide.
Our approach to investing is scientific: we bring together data, research, technology and trading expertise to develop and run systematic strategies.
Technology is central to how QRT works. Over the years, we have built a global research and execution platform spanning geographies, asset classes and trading horizons, from ultra-low-latency systems to large-scale research, analytics and risk infrastructure.
We invest heavily in engineering, automation and scalable systems because better technology allows us to test ideas faster, operate more reliably and tackle harder problems.

Equal Opportunity

QRT is an equal opportunity employer.
We welcome diversity as essential to our success.
QRT empowers employees to work openly and respectfully to achieve collective success.