Utilize relevant tools in data engineering and programming to build systems that collect, manage and transform data into usable information to ensure the reliability, efficiency, and scalability of the organization’s data infrastructure, thereby enabling robust data-driven decision-making and operational efficiency.

Main Accountabilities

• Construct and optimize datasets (Big Data) pipeline and architecture to handle large volumes of data for business, monetary and supervisory policies decisions.
• Develop processes to support data transformation, work-load management, data structures, dependencies, and metadata.
• Provide first line technical support to the Data Warehouse, surveys and electronic Balance of Payment (BoP) monitoring systems to ensure continuous systems availability.
• Undertake development/conversion/re-engineering of data survey systems that align with data warehouse protocols and user needs.
• Collaborate with data scientists, economists, data analysts, and other stakeholders to understand data needs and facilitate data driven decision making support.
• Build and maintain data systems and construct datasets that are easy to analyze and support the Bank’s diverse needs and requirements whilst undertaking initiatives that improve data reliability and quality.
• Create consistent and machine-readable formats, develop and test architectures that enable data extraction and transformation for predictive or prescriptive modelling.
• Identify opportunities for data acquisition and develop analytical tools and programs to support decision making.
• Collaborate with data scientists and architects on several projects and combine raw information from different sources to create a single repository of data for the Bank.

Qualifications and Experience

• Master’s degree in Data Science, Software Engineering or equivalent
• Three (3) years relevant experience
• Grade 12 School Certificate with a minimum of five (5) ‘O’ Levels (Credit or better)

Key Knowledge and Attributes

• Programming Skills – knowledge of statistical programming languages like R, Python and database query languages like SQL, Hive and Pig is desirable. Familiarity with Scala, Java, or C++ is an added advantage;
• Quantitative skills – Good applied statistical skills, including knowledge of descriptive and inferential statistics, including Bayesian probability computations;
• Machine Learning – Familiarity with ML platforms such as TensorFlow, Azure, IBM Watson, etc coupled with good knowledge of machine learning methods like k-Nearest Neighbours, Naive Bayes, SVM, Decision Forests;
• Experience with data Visualization tools like Power BI, matplotlib/seaborn, ggplot2, d3.js., Tableau that help to visually encode data;
• Ability to plan, create, program and manage complex statistical computer databases;
• Strong communication (spoken, written and presentational) skills;
• Knowledge of structured and object-oriented systems methodologies;
• Out of Box Thinking – Looks for, and proposes, a variety of opportunities and solutions for a unique and innovative approach; and
• Results oriented – demonstrate the ability to set and manage to achieve goals.