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Reporting to the Head of Data Analytics, QEP, the successful candidate is expected to be highly technical to work, in the first instance, with our research data however it is expected that many of the techniques and building blocks used to build it will be transferrable to the production dataset.
Build and maintain a scalable database for time series
Validation and automatic/ manual correction of errorneous data
Quantitative analysis of investment signals using traditional, sparse and unstructured data sources
Undertake ad-hoc development, implementing bug fixes, BAU enhancements and process improvements as required
Follow IT standards for documentation & system access
Great problem solving and technical skills
Ability to deal credibly with business and technical users at all levels of the organisation
Ability to operate under pressure and deliver to demanding deadlines
A genuine team player
Ability to adapt to rapidly changing requirements
Strong self-organisation, time management and prioritisation skills
Inter-personal skills; tact, patience, courtesy, good listening skills
Excellent verbal and written communication skills and a service-orientated approach coupled with a "can-do" attitude
Experience of building highly scalable and high performance databases
Strong knowledge large scale database technologies (such as Data Marts/ Cubes, Time Series, Cloud)
Strong knowledge of MS SQL Server, T-SQL
Knowledge of at least one Data Analytics programming language (such as Matlab/ R/ Python etc)
A proven ability to pro-actively identify erroneous data
Good knowledge (or the desire to obtain) of financial datasets
A good understanding of equity capital markets/ products
A good understanding of fundamental accounting data
An understanding of Quantitative Equity Strategies and their implementation
Building Cloud enabled systems
Matlab, .Net, VBA / Excel
Experience of using large scale data analysis stack (Spark, Hive HQL etc..)