Position: Quant Developer with Equity Risk (Python/Data Modelling)
Company: Luxoft
Category: Python
Experience required: 5y
English level: intermediate

Project Description:
There are several ongoing projects for one of our clients in Abu Dhabi. This particular project will involve working on the integration of the Risk engine developed internally using MatLab and one component of the client's data platform (Foundry) written in Python.The client is a sovereign wealth fund owned by Emirate of Abu Dhabi founded for the purpose of investing funds on behalf of the Government of the Emirate of Abu Dhabi. It manages the Emirate's excess oil reserves.
The Client uses many different bespoke and existing products and platforms to run the business.
Our team is small but growing fast, now it consists of several ReactJS and C# developers.
Responsibilities:
Responsibilities are (but not limited to) -
• Participate in requirements clarification and sprint planning sessions
• Design technical solution (to integrate risk system developed on MatLab)
• Writing unit and integration tests
• Coach/mentor new joiners
• Define best practices
Mandatory Skills:
• Quant - experience in financial domain; (with background in either
        fixed income/equity or risk will be considered)
• Language: 5+ years of strong experience in Python / SQL;
• Must have experience with building/maintaining risk engine;
• Working experience working with MatLab
• Databases: Relational (SQL), Non-relational (Mongo, Cassandra);
• Modelling: Good understanding of Entity Relationship Diagram;
• Data Pipelines: Experience with data cleansing, data profiling, data
        lineage, best practices;
Nice-to-Have Skills:
• Strong in Mathematics;
• Worked in front office
• Experience with MatLab Production;
• Knowledge of equity risk
• Databases: Understanding of Map Reduce and algorithms to aggregate large amounts of data;
• Modelling: knowledge of data modelling design patterns;
Languages: PySpark;
• Databases: Understanding of Map Reduce and algorithms to aggregate large amounts of data;
• Modelling: knowledge of data modelling design patterns;
• Domain knowledge: In-depth exposure to Financial market (Instruments, trade life cycle, Risk measures)