Financial Data Analyst - Python

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world.

We are a technology and data driven group implementing a scientific approach to investing.

Combining data, research, technology, and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges.

QRT’s culture of innovation continuously drives our ambition to deliver high-quality returns for our investors.

We are looking for an exceptional Data Analyst to join our team.

In this role you will collaborate closely with Researchers, Analysts, and Engineering teams to ensure the timely delivery, quality, and effective use of financial data across the firm's research and trading platform.

Your future role within QRT

• Develop and maintain Python-based data pipelines to ingest, transform, validate, and load external financial datasets.
• Onboard new datasets by understanding vendor documentation, mapping data fields, defining schemas, performing historical backfills, and deploying production-ready data feeds.
• Own assigned datasets throughout their lifecycle, monitoring data pipelines, resolving data delivery issues, and improving reliability.
• Investigate and resolve data quality issues such as missing data, duplicates, stale values, outliers, and identifier or calendar inconsistencies.
• Work with external data vendors to resolve delivery issues, clarify data definitions, and manage format or coverage changes.
• Document datasets, transformation logic, and operational processes to support long-term maintainability.
• Collaborate with researchers, analysts, and engineers to deliver reliable datasets that meet business requirements

Your present skillset

• 3+ years of experience in Financial Data, Market Data, Data Engineering, Data Operations, or a similar data-focused role.
• Bachelor's or master's degree in a quantitative discipline such as Mathematics, Physics or Engineering.
• Advanced programming experience in Python, including proficiency with data handling libraries such as Pandas and NumPy
• Experience working with structured and semi-structured data such as CSV, JSON, XML, Parquet, APIs, or vendor data feeds.