Intermediate Data Engineer

Boardroom Appointments; Durban; On-site; ZAR 600,000 - 800,000; Full time

Job summary

A recruitment agency is seeking an Intermediate Data Engineer in Durban, South Africa. The successful candidate will have a strong background in Scala or Java, experience with cloud platforms like AWS, GCP, or Azure, and a solid understanding of data modeling principles. Responsibilities include building scalable data infrastructure, developing APIs for data science outputs, and optimizing cloud costs. The ideal candidate will possess a Master's in a related field and have 2-3 years of relevant experience.

Qualifications

• Matric (Grade 12) is required.
• 2-3 years of relevant work experience is a must.
• Knowledge of API development and machine learning deployment.

Responsibilities

• Build and scale data infrastructure for real-time data processing.
• Create scalable data ingestion and machine learning inference pipelines.
• Develop APIs to deliver data science outputs.
• Scale production systems for increased demand.
• Provide visibility into data platform health.
• Automate and handle life-cycle of data processing systems.

Skills

Scala

Java

AWS

GCP

Azure

Data modeling

Relational databases

NoSQL databases

Spark

Hadoop

Kafka

Docker

Kubernetes

Education

Masters in Software Engineering, Data Engineering, Computer Science or related field

Tools

Postgres

ElasticSearch/OpenSearch

Graph databases

Job description

About the job Intermediate Data Engineer

• Matric (Grade 12)
• Masters in Software Engineering, Data Engineering, Computer Science or related field
• 2-3 years of relevant work experience
• Strong Scala or Java background
• Knowledge of AWS, GCP, Azure, or other cloud platform
• Understanding of data modeling principles
• Ability to work with complex data models
• Experience with relational and NoSQL databases (e.g. Postgres, ElasticSearch/OpenSearch, graph databases such as Neptune or neo4j)
• Experience with technologies that power analytics (Spark, Hadoop, Kafka, Docker, Kubernetes) or other distributed computing systems
• Knowledge of API development and machine learning deployment

Responsibilities:

• Build and scale data infrastructure that powers real-time data processing of billions of records in a streaming architecture
• Build scalable data ingestion and machine learning inference pipelines
• Build general-purpose APIs to deliver data science outputs to multiple business units
• Scale up production systems to handle increased demand from new products, features, and users
• Provide visibility into the health of our data platform (comprehensive view of data flow, resources usage, data lineage, etc) and optimize cloud costs
• Automate and handle the life-cycle of the systems and platforms that process our data