Provide infrastructure, tools and frameworks used to deliver end-to-end solutions to business problems. Build scalable infrastructure for supporting the delivery of clear business insights from raw data sources; with a focus on collecting, managing, analysing, visualising data and developing analytical solutions.Responsible for expanding and optimising Standard Bank's data and data pipeline architecture, whilst optimising data flow and collection to ultimately support data initiatives.
Qualifications
Type of Qualification:
Degree in Information Technology or equivalent
Experience Required
Data & Analytics
5-7 years
Experience with big data tools:
Hadoop, Spark, Kafka, etc. Experience with relational SQL and NoSQL databases, including Postgres and Cassandra. Experience with data pipeline and workflow management tools: Azkaban, Luigi, Airflow, etc. Experience with AWS cloud services: EC2, EMR, RDS, Redshift. Experience with stream-processing systems: Storm, Spark-Streaming, etc. Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc.
5-7 years
Strong analytic skills related to working with unstructured datasets. Build processes supporting data transformation, data structures, metadata, dependency and workload management. A successful history of manipulating, processing and extracting value from large disconnected datasets. Working knowledge of message queuing, stream processing, and highly scalable 'big data' data stores.
5-7 years
Working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases. Experience building and optimizing 'big data' data pipelines, architectures and data sets. Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
Additional Information
Behavioural Competencies:
Adopting Practical Approaches
Articulating Information
Challenging Ideas
Checking Things
Examining Information
Exploring Possibilities
Interpreting Data
Meeting Timescales
Producing Output
Providing Insights
Team Working
Upholding Standards
Technical Competencies:
Data Analysis
Database Administration
Data Integrity
Knowledge Classification
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