Acting as a technical lead within a confidential asset management company with assets under management of €800bn, I’m helping industrializing Big Data Processing.

• Collaborated with the quantitative analysts team on a significant data project, resulting in substantial savings for the company by optimizing resource allocation.
• Leveraging the capabilities of Scala and Spark to manage high-volume data processing
• Optimizing data storage in HBase, achieving significantly lower latency for random access and improving overall system performance.
• Maintaining and enhancing legacy APIs using Java Play, ensuring seamless on-premises operations.
• Architected and implemented a next-generation REST API infrastructure from scratch using FastAPI on Azure, successfully handling over 100K requests daily with high availability and performance.

• Daily calculation of VaR on open funds (26 funds, AUM €1bn) with StatPro.
• Conduct extensive statistical studies to ensure that the methodologies used are correct and in line with good practice.
• Create checks with Python, Pandas backed by a SQLite instance to ensure positions between the custodian and StatPro are in line. The central database is easier and safer to administer and control and allows to run SQL queries.
• Create concurrent and highly scalable data pipelines on the top of AWS Lambda.
• Create Python applications connected to the database to output a look through transparency of funds of funds, compute long-term performance of composite benchmarks (chaining them if necessary), generate PDF reports to study correlation between asset classes (ReportLab and StatsModels packages).
• Prepare and attend Risk Committees to discuss current market trends, credit risks, political risks and ongoing developments.
• Create an in-house Python Library to implement mass risk reporting using our SQLite Database. Reports are generated with ease and the program takes care of specificities and calculations. Ubiquitous metrics like volatility, beta, Sharpe ratio, Treynor ratio, TE, information ratio and Jensen alpha are reported.

• Create 8 Applications (VBA) with Access (Back-end DB) and Excel (Front-end interface) to improve efficiencies in managing large amount of data. The roll-out of the applications contribute to reduce the risk of error, save time and facilitate a greater communication amongst the investment team. Portfolio Managers are using these tool to review and adjust their allocation.
• Design application to automatically extract the Performances and Asset under Management (“AUM”) on a monthly basis across 300 Institutional Mandates (AUM €30bn).
• Automate new online Fixed Income dashboards allowing direct access to market data (VBA and FactSet).
• Create checks & controls procedures on performances and Net Asset Values to detect inconsistencies. Ensure AuM across different views are reported in an identical fashion (by mandates, by asset class, by countries…). Compare AuM changes versus market performances to identify trend and issues.