Job description

The configuration and implementation of Quintessence at our various clients.

  • Understand the business requirements of Quintessence’s clients, the focus being the research and investment process of these Asset Managers.
  • Construct end to end data service solutions
  • Liaise and Interface with clients in a support role, providing 2nd Tier support and enhancement services
  • Understand and manage the client’s data requirements, the data being specific to the financial markets.
  • Contribute towards a team that develops, constructs, tests and maintains architectures (such as data bases and large-scale processing systems)
  • Ensure data architecture will support the requirements of the client’s business
  • Employ a variety of languages and tools (e.g. scripting languages) to marry systems together
  • Recommend ways to improve data reliability, efficiency and quality
  • Employ sophisticated analytics and statistical methods to prepare data for use in prescriptive modelling
  • Automate work by using process flow tools
  • Provide feedback to the Development team regarding new functionality and issue logging
  • Creation of user interfaces allowing users to upload their own data
Technical Skills / Expertise

  • Data analysis, modelling and surfacing
  • Data cleaning / Integrity checking • Experience of creating reports using Excel or equivalent
  • SQL, SSIS, database scripting (stored procedures, user defined functions, queries, triggers)
  • Iterative testing including debugging and refactoring
  • Constructing data queries by combining multiple data sources
  • Present information using data visualization techniques (such as QlikView, PowerBI and Tableau) • Experience of consuming APIs (advantageous)
  • Some experience in a programming language (advantageous) • Any sort of ETL or Data Warehousing knowledge (advantageous)
  • Statistical languages (such as R and Matlab) (advantageous
Preferred Qualifications & Experience:
  • Tertiary degree in BSc Computer Science, B.IT or Informatics related degrees, Mathematics, Applied Mathematics, Actuarial Science or an Engineering degree.
  • Understanding and working experience in data integration and transformation

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