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Data Analyst - BSTD at South African Reserve Bank
South African Reserve Bank
September 02, 2026
Full-time
On-site
Brief description
The main purpose of this position is to manage, govern and analyse the data ecosystem, ensuring comprehensive insights and driving data rationalisation to enable informed and effective decision-making across the South African Reserve Bank (SARB).
Detailed description
The successful candidate will be responsible for the following key performance areas:
Discover data by applying data discovery mechanisms to establish datasets within the context of the business value drivers, processes and data products across the SARB.
Identify the data profiles of discovered datasets to determine completeness and compliance, and to support inventory management.
Implement standards, guidelines, processes and procedures for data analysis within the SARB.
Establish relationships between data elements and assess their impact on the business.
Conduct data analysis to determine various categories of data and information.
Apply data valuation techniques to determine the importance of the data to the business and the likelihood of reuse.
Generate regular reports, deliver actionable insights and provide timely status updates and information to support the management of data domains.
Develop and manage stakeholder relations effectively to promote data management across the organisation and support reliable decision-making and appropriate data usage.
Provide inputs, such as legislative prescripts, sensitivity classifications and access and authorisation rules, as data transitions from source to destination.
Job requirements
To be considered for this position, candidates must have:
a Bachelor's degree (NQF 7) in Mathematical Sciences, such as Actuarial Science, Applied Mathematics, Computer Science, Computer Engineering, Data Science, Economics, Informatics, Statistics or an equivalent qualification;
at least five to seven years' experience in a data analysis environment; and
an Honour's degree and/or a relevant data analytics certification will be an advantage.
Additional requirements include:
knowledge and skill in:
industry, organisational and business awareness;
continuous learning and/or professional development;
quality assurance;
information management;
enterprise information management (EIM) strategy;
data quality management;
metadata management;
data modelling and development;
information governance; and
EIM legislation and governance, risk and compliance;
best practices and methodologies in data warehousing and multi-dimensional data modelling (OLAP), including tools such as ESSBASE and Microsoft Analysis Services; and
experience in data manipulation statistical tools such as SQL, Python, R, SPSS, Microsoft Excel, MATLAB, SAS and exposure to additional languages, including but not limited to Java and C#;
knowledge of database management systems and tools (e.g. IBM DB2, Oracle Database, Microsoft SQL Management Studio) and exposure to cloud-based databases; and
experience in using a broad variety of integration techniques, patterns, tools and methodologies, additional exposure to deploying and managing big data environments (e.g., Hadoop, Apache Spark, and NoSQL) will all be advantageous.
The main purpose of this position is to manage, govern and analyse the data ecosystem, ensuring comprehensive insights and driving data rationalisation to enable informed and effective decision-making across the South African Reserve Bank (SARB).
Detailed description
The successful candidate will be responsible for the following key performance areas:
Discover data by applying data discovery mechanisms to establish datasets within the context of the business value drivers, processes and data products across the SARB.
Identify the data profiles of discovered datasets to determine completeness and compliance, and to support inventory management.
Implement standards, guidelines, processes and procedures for data analysis within the SARB.
Establish relationships between data elements and assess their impact on the business.
Conduct data analysis to determine various categories of data and information.
Apply data valuation techniques to determine the importance of the data to the business and the likelihood of reuse.
Generate regular reports, deliver actionable insights and provide timely status updates and information to support the management of data domains.
Develop and manage stakeholder relations effectively to promote data management across the organisation and support reliable decision-making and appropriate data usage.
Provide inputs, such as legislative prescripts, sensitivity classifications and access and authorisation rules, as data transitions from source to destination.
Job requirements
To be considered for this position, candidates must have:
a Bachelor's degree (NQF 7) in Mathematical Sciences, such as Actuarial Science, Applied Mathematics, Computer Science, Computer Engineering, Data Science, Economics, Informatics, Statistics or an equivalent qualification;
at least five to seven years' experience in a data analysis environment; and
an Honour's degree and/or a relevant data analytics certification will be an advantage.
Additional requirements include:
knowledge and skill in:
industry, organisational and business awareness;
continuous learning and/or professional development;
quality assurance;
information management;
enterprise information management (EIM) strategy;
data quality management;
metadata management;
data modelling and development;
information governance; and
EIM legislation and governance, risk and compliance;
best practices and methodologies in data warehousing and multi-dimensional data modelling (OLAP), including tools such as ESSBASE and Microsoft Analysis Services; and
experience in data manipulation statistical tools such as SQL, Python, R, SPSS, Microsoft Excel, MATLAB, SAS and exposure to additional languages, including but not limited to Java and C#;
knowledge of database management systems and tools (e.g. IBM DB2, Oracle Database, Microsoft SQL Management Studio) and exposure to cloud-based databases; and
experience in using a broad variety of integration techniques, patterns, tools and methodologies, additional exposure to deploying and managing big data environments (e.g., Hadoop, Apache Spark, and NoSQL) will all be advantageous.