FULLTIME : Erwin Data Modeler

Delivery focussed Senior Data Modeller (Erwin)

• We are seeking an experienced Senior Data Modeller to design, govern, and implement enterprise data models for strategic data objects within the data platform. 
• This role is pivotal in transforming fragmented legacy schemas into unified, canonical data assets. 
• A strong delivery focus, coupled with close collaboration with business and technical teams, is essential to operationalize robust data models for Risk, Finance, analytics, and AI consumption.

Key Responsibilities:

• Design and maintain conceptual, logical, and physical enterprise data models using Erwin Data Modelling tool.
• Lead the reverse engineering of legacy databases to consolidate fragmented schemas into canonical enterprise objects.
• Define and enforce modelling standards, keys, and relationships to drive convergence of legacy systems.
• Implement temporal modelling patterns, including SCD, effective-dating, historisation, and EoD snapshots for critical reporting and analytics.
• Ensure data models support scalable analytics, AI, and robust metadata management while balancing accuracy with performance.
• Collaborate with stakeholders and the enterprise modelling team to align with governance standards. 

Required Skills & Experience:

• Mandatory: Strong hands-on expertise with Erwin Data Modeler and SQL, including conceptual/logical/physical modelling and reverse engineering.
• Mandatory: Proven track record of delivering data models, working closely with business, development, and testing teams.
• Mandatory: Extensive experience modelling financial services domains (securities, trades, positions, pricing, accounts, counterparties) for large-scale data platforms, with a focus on performance and storage efficiency.
• Deep expertise in temporal modelling (SCD, effective dating, historisation, snapshot models).
• Ability to challenge existing designs and collaborate with architects and domain experts to enhance data models.
• Experience rationalizing and converging multiple legacy schemas into strategic canonical data objects.