Standard Bank Group
Develop and Maintain Scalable Data Infrastructure
Assist in creating and maintaining optimal data pipeline architecture, assemble large and complex data sets, and create databases optimized for performance across Standard Bank platforms.
Enable Data-Driven Insights
Build analytics tools that utilise data pipelines to provide actionable insights into customer acquisition, operational efficiency, and other key business metrics. Create data tools to support analytics and data science teams.
Support Data Migrations and Transformations
Assist in enabling and running data migrations across different databases and servers, perform thorough testing and validation to support data transformation accuracy, and contribute to machine learning model development.
Design and Develop ETL and Big Data Solutions
Design and develop scalable ETL packages and routines to populate databases, manage large-scale Hadoop platforms, and support growing data needs within the business.
Conduct Analysis and Optimisation
Perform ad-hoc data analyses, write SQL scripts, stored procedures, and proactively evaluate databases to identify and recommend improvements and optimisation strategies.
Collaborate Across Data and Business Teams
Liaise with the EDO team and collaborate with subject matter experts to select relevant data sources and translate business requirements into data mining and science outcomes.
Automate and Improve Internal Data Processes
Utilise data under supervision to discover tasks that can be automated and identify, design, and implement internal process improvements, such as automating manual tasks and re-designing infrastructure for scalability.
Qualifications
Type of Qualification: First Degree
Field of Study: Information Studies
Type of Qualification: First Degree
Field of Study: Information Technology
Experience Required
Data Monetisation
Data & Analytics
3-4 years
Experience in working with unstructured data (e.g. Streams, images) Understanding of data flows, data architecture, ETL and processing of structured and unstructured data. Using data mining to discover new patterns from large datasets. Implement standard and proprietary algorithms for handling and processing data. Experience with common data science toolkits, such as SAS, R, SPSS, etc. Experience with data visualisation tools, such as Power BI, Tableau, etc.
3-4 years
Proven development experience in software and software engineering. Understanding of financial services data processes, systems, and products. Experience in technical business intelligence. Knowledge of IT infrastructure and data principles. Project management experience. Experience in building models (credit scoring, propensity models, churn, etc.)
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