Investment Data Modeler – Investment Data Modeling

Job Description

Investment Data Modeler | Generali

The Tone:
This is a full-time role at Generali, located within the Group Head Office – Global Investment Management (GIM) Function. The company is a multinational insurance group, and this role contributes to its robust data infrastructure by supporting the data modelling activities of the group data warehouse. The Investment Data Modeler is critical for ensuring data consistency and accuracy across all investment domains, enabling effective reporting, risk management, and accounting.

The TL;DR
• Role: Full Time
• Type: Full-time
• Location: In-person – Group Head Office

• Team: Investment Reporting and Data team within Global Investment Management (GIM)
• Mission: To design and evolve the investment data model for the group data warehouse, ensuring consistency and scalability.
• Tech Stack: Excel, PowerPoint, SQL

What You’ll Actually Do
• Data Model Design: Take care of the design and evolution of the investment data model across all relevant domains.
• Structure Definition: Define precise data structures for instruments, portfolios, positions, pricing, and performance data.
• Data Consistency: Drive consistency of data definition across reporting, risk, and accounting functions within the group.
• Process Translation: Translate complex investment processes into efficient and scalable data models to support strategic initiatives.
• Cross-Functional Collaboration: Act as the key bridge between the Investment Office, IT, and Data teams to facilitate data-related projects.

The Must-Haves
• Background: Master’s degree in Economics, Finance, Engineering, Management, Computer Science, or a related quantitative field, demonstrating foundational knowledge in investment data domains.
• Experience: At least 5 years of experience specifically within investment data domains, gained from a multinational insurance company, asset manager, or bank.
• Skills: Deep knowledge of data modelling on investment data, strong SQL language proficiency, and advanced skills in Excel and PowerPoint. This includes a clear understanding of business intelligence models, financial instruments, referential data, and main risk measures.
• Bonus: Any experience with programming languages such as SAS, Python, or R is a plus, as is knowledge of any additional languages beyond fluent English.