Data Developer

Rimes provides the Intelligence Fabric for Capital Markets, a trusted data network and intelligence architecture that transforms fragmented data, operations and workflows into decision-grade intelligence. The world’s leading institutional investors, asset managers, and service providers rely on Rimes to help them make better investment decisions that power more than US$ 75 trillion in AUM annually.

The Data Team at Rimes delivers cloud-based financial datasets that support critical investment decisions for institutional clients globally. 

As a Data Developer, you will design and operate data pipelines on modern cloud platforms, transforming raw vendor data into validated, production-ready datasets. You will contribute to the scalability, reliability, and accuracy of the Rimes data platform, supporting both standardised and client-specific solutions. 

• →Build and maintain scalable data pipelines using Databricks and PySpark to ingest, transform, and deliver financial datasets  
• →Ensure datasets are accurate, consistent, and validated, supporting downstream analytics and reporting  
• →Integrate and manage data from global vendors, adapting to feed changes and evolving data requirements  
• →Translate business and client requirements into robust, production-ready data workflows  
• →Partner with product and client teams to deliver data outputs aligned to agreed timelines and quality standards  
• →Contribute to solutions supporting multiple asset classes and investment workflows  
• →Operate within structured processes and tooling to ensure traceability, control, and operational resilience of data pipelines 

• Strong programming skills in Python and PySpark  
• Hands-on experience working with Databricks or similar distributed data processing platforms  
• Familiarity with version control systems (e.g. Git)  
• Degree in a technical discipline (e.g. Engineering, Computer Science, Mathematics)  
• At least 1 year of experience working with data in a technical or project-based context  
• Experience working with structured or semi-structured data, with an emphasis on data transformation and validation  
• Strong problem-solving skills with a high level of accuracy and attention to detail  
• Able to manage multiple tasks and priorities effectively  
• Clear and effective communication skills  
• Comfortable working independently while collaborating across distributed teams 

• Experience with SQL and data lake / lakehouse architectures  
• Familiarity with data formats such as Parquet, XML, and ingestion via APIs  
• Exposure to platforms such as Snowflake or Foundry (Palantir)  
• Understanding of financial markets or financial datasets  
• Experience using Excel for data analysis or validation  
• Progress toward CFA or equivalent financial qualification