Cloud Data Engineer: Build Scalable Azure Data Platforms

DeARX; Sandton; On-site; ZAR 900,000 - 1,300,000; Full time

Job summary

DeARX in Sandton is seeking an experienced Cloud Data Engineer to design, build and optimise secure, scalable cloud data platforms and pipelines supporting analytics, AI, and decision making. You will work with Microsoft Azure, Databricks and modern data engineering tools to develop Data Lakes and Data Warehouses, implement DataOps and ensure data quality, governance and security across the enterprise.

Collaborate with engineers, data scientists and stakeholders to deliver reliable data products

Qualifications

• Bachelor’s Degree in Information Technology, Computer Science or a related field.
• 2–5 years’ experience in Data Engineering, Business Intelligence or Software Engineering.
• Experience building enterprise cloud data solutions.
• Experience working in Agile delivery environments.

Responsibilities

• Designing and developing scalable cloud data pipelines for batch and real-time processing.
• Building robust ETL/ELT solutions using Azure and modern data engineering technologies.
• Developing enterprise Data Lakes and Data Warehouses.
• Working with Microsoft Fabric, Azure Data Factory, Databricks and Azure cloud services.
• Developing data ingestion frameworks for structured, semi-structured and unstructured data.
• Implementing DataOps practices, CI/CD pipelines and workflow orchestration.
• Ensuring data quality, governance, lineage and metadata management.
• Collaborating with Software Engineers, Data Scientists, Architects and stakeholders to deliver data products.
• Supporting cloud security, data privacy and POPIA compliance.

Job description

DeARX in Sandton is seeking an experienced Cloud Data Engineer to design, build and optimise secure, scalable cloud data platforms and pipelines supporting analytics, AI, and decision making. You will work with Microsoft Azure, Databricks and modern data engineering tools to develop Data Lakes and Data Warehouses, implement DataOps and ensure data quality, governance and security across the enterprise.

Collaborate with engineers, data scientists and stakeholders to deliver reliable data products