Data Modelling Engineer

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.

As a Data Modelling Engineer, you will design scalable, production-ready data models for financial datasets on Databricks, and make sure they are understood, structured correctly, and ready for downstream use. Alongside building data models, you will perform practical, early‑stage analysis on new datasets, checking fields, formats, and structure to ensure consistency and proper handling before the data enters our pipelines.  

You will help ensure that new datasets, including index, pricing, reference, and research data, integrate smoothly into Rimes’ data ecosystem with clarity, quality, and traceability. This role is well suited for someone who enjoys rolling up their sleeves, exploring datasets directly, and shaping the underlying structures that power data workflows across the organization. 

• →Design and maintain scalable data models (tables, relationships, hierarchies, history handling) on Databricks using Delta Lake and Lakehouse architecture. 
• →Profile new index, pricing, reference, ESG, and research datasets to understand structure, fields, data types, keys and vendor specific quirks. 
• →Identify inconsistencies, missing fields, formatting issues, or schema gaps and collaborate on remediation. 
• →Define source-to-target mappings, schemas and handling rules that Data Engineering can implement in ETL/ELT pipelines, and work closely with them to ensure the build matches the design. 
• →Test sample data against models to validate mappings and assumptions before they go into production. 
• →Use Unity Catalog to support governance, lineage and traceability of modelled data. 
• →Document field definitions, data relationships, and handling rules clearly for internal teams, and contribute to modelling standards and good data practice.   
• →Review provider schema updates and adjust models when needed to maintain alignment.   

• 5+ years of experience in data modelling, with a track record of designing conceptual, logical and physical models for production platforms. 
• Solid grasp of relational and dimensional modelling fundamentals, including hierarchies and history handling (e.g. slowly changing dimensions) 
• Strong SQL and hands-on experience profiling and validating unfamiliar datasets. 
• Hands-on experience with Databricks, including Delta Lake and Unity Catalog. 
• Python for data profiling and validation. 
• Experience with Git or similar version control systems.   
• Degree in a technical discipline (e.g. Engineering, Computer Science, Mathematics) or equivalent experience demonstrating strong analytical and data modelling capability.  
• Strong attention to detail and ability to investigate datasets independently.   
• Clear communication skills and comfort working with cross‑functional teams.   
• Organized, proactive, and committed to accuracy and continuous improvement.  
• Experience working with financial data, including research datasets (e.g. ESG data), with the ability to model data for analytical and investment use cases. 

• PySpark for data exploration, prototyping or building transformations. 
• Familiarity with common data exchange and schema formats, such as XML and JSON.   
• Experience with modern data platforms such as Palantir Foundry or Snowflake. 
• Strong Excel skills for exploratory data assessment.   
• Progress toward the CFA or a similar investment qualification, or equivalent knowledge of investment data and concepts