Data Modeling Engineer

About Rimes

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 Opportunity:

As a Data Modeling Engineer, you will work hands‑on with financial datasets to 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. 

Responsibilities: 

• Inspect new datasets to understand their structure, fields, data types, and quirks before they are ingested.  
• Identify inconsistencies, missing fields, formatting issues, or schema gaps and flag what needs adjustment.  
• Build and update data models (tables, relationships, hierarchies) based on real dataset behavior.  
• Test sample data against your models to ensure mappings, assumptions, and transformations are correct.  
• Work closely with Data Engineering to translate your models into ETL/ELT‑ready schemas.  
• Document field definitions, data relationships, and handling rules clearly for internal teams.  
• Review provider schema updates and adjust models when needed to maintain alignment.  
• Support data quality improvements by applying model‑level fixes and ensuring consistent patterns.  
• Contribute to straightforward improvements to modeling standards, documentation, and internal processes. 

Requirements: 

• Experience working with conceptual, logical, and physical data models.  
• Solid understanding of relational and dimensional modeling basics.  
• Proficiency in SQL and familiarity with Python for profiling, validation, or simple automation.  
• Experience with Git or similar version control systems.  
• Educational background in a technical discipline (e.g. Engineering, Computer Science, Mathematics) or equivalent experience demonstrating strong analytical and data modeling capability.