Build and maintain robust data infrastructure and working closely with the quant, IT, Enterprise Data and vendors.
Ensure that the data infrastructure and related processes are consistent with best practice, well documented and adhere to internal policies and regulatory requirements.
Improve productivity and robustness of workflows by automating, stabilizing, and streamlining
Source, clean, store and analyse data internally and external vendors as required
Strong documentation and guides for the benefit of team members and for audit requirements.
Support portfolio managers in developing and enhancing index strategies and beta solutions
The candidate should be a highly motivated, junior to mid career professional with 2-8 years experience who is initiative and wide array of skills and industry knowledge necessary to accomplish the variety of tasks associated with the role.
Success in the role depends upon the candidate’s ability to build, maintain and support a robust data infrastructure ultimately helping the team to meet their day to day challenges.
EXPERIENCE / SKILLS
Bsc or above in Computer Science or a quantitative discipline.
At least 2 years of experience in the financial industry. Relevant experience within an asset management or hedge fund environment a plus.
Knowledge of database design, networking protocols and data structures
Ability to design, create, maintain databases and stored procedures
Demonstrated ability to organize, maintain and integrate a range of data feeds from multiple channels in different formats
Excellent SQL and proficient Python required. Knowledge of R/ C#/ Perl a plus.
Working knowledge of SQL Server and database management tools a plus.
Working knowledge of APIs for financial information systems (e.g. Factset, Refinitiv) a bonus
Familiarity with Aladdin system, modules and data a plus
Understanding of investment management principles and financial concepts.
Knowledge of systematic factor-based investment strategies and risk models (e.g. Axioma) a plus
Build and deliver robust solutions on time, work productively with systematic investment and IT/Enterprise Data teams.
Familiar with best practices in change management, release management, and incident management.
Object-oriented design skills with knowledge of design patterns and object modelling for building business logic, analytics and data processing applications.
Experience with machine learning and data visualization techniques a plus.