Junior Data Engineer

The Junior Data Engineer is responsible for supporting development and delivery of end-to-end data solutions. The incumbent supports solution envisaging and technical designs, and drives hands-on implementation. Supports the influencing, differentiation, and guidance to business and technology strategies, as they relate to data, through constant cross-functional interaction.

Key Responsibilities

• Drive efficiency and resilience by mapping data flows between systems and workflows across the company

  
  

• Ensure standardisation by following design patterns in line with global and local data governance requirements

  
  

• Support real- time internal and customer- facing actions by developing real-time event- based streaming data pipelines

  
  

• Enable rapid data- driven business decisions by supporting development of efficient and scalable data ingestion solutions

  
  

• Drive high- value data by connecting different disparate datasets from different systems

  
  

• Monitor and follow relevant SLIs and SLOs

  
  

• Handle, mitigate and learn from incidents in a manner that improves the overall system health

  
  

• Ensure ongoing resilience of data processes by monitoring system performance and acting proactively identifying bottlenecks, potential risks, and failure point that might degrade overall quality

  
  

• Write readable and reusable code by applying standard patterns and using standard libraries

  
  

• Continuously evolve own craft by keeping up to date with the latest developments in data engineering and related technologies, and upskilling on these as needed.

  
  

• Responsible for technical implementation and maintenance of data processing services and storage systems in line with the Data Governance Framework.

  
  

Requirements of special knowledge/skills

• Entry level exposure to a data engineering or related field using a server side programming languages, preferably Scala, Java, Python, or Perl

  
  

• Entry level exposure to building data pipelines and transformations at scale, with technologies such as Hadoop, Cassandra, Kafka, Spark, HBase, MySQL

  
  

• Basic knowledge of data modeling methods based on best practices, e.g. TOGAF

  
  

• Basic knowledge of data quality requirements and implementation methodologies

  
  

• Excellent English communication skills, both written and verbal.