Data Modeling Engineer

Rimes provides enterprise data management solutions to the global investment community. Driven by our passion for solving the most complex data problems, we provide our clients with investment intelligence that powers more than US$75 trillion in assets under management annually. The world’s leading institutional investors, asset managers and service providers rely on Rimes to help them make better investment decisions using accurate information and industry-leading technology.

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. 

• →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. 

• 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. 
• 1–3+ years of experience in data modeling, data engineering, or data architecture.  
• 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. 

• Familiarity with XML, JSON, and schema definition languages.  
• Experience with modern data platforms such as Databricks, Foundry (Palantir), Snowflake, or Parquet‑based systems.   
• Strong Excel skills for exploratory data assessment.  
• Progress toward CFA Level II or higher.