Cloud Data Engineer (Johannesburg)

Our client is seeking a highly skilled Cloud Data Engineer to join their growing data and cloud technology team. The successful candidate will be responsible for designing, developing, and optimizing cloud-based data solutions that support enterprise analytics, reporting, and data-driven decision-making. This role requires strong expertise in cloud platforms, data integration, and modern data engineering practices, with hands-on experience in technologies such as Microsoft Fabric, AWS, Databricks, and cloud-native data services.

The cloud data engineer will be responsible for building scalable data pipelines, implementing robust data architectures, and ensuring the secure and efficient movement of data across cloud environments. The ideal candidate will have experience working with structured and unstructured data, data warehousing, ETL/ELT processes, and cloud-based analytics platforms.

Responsilities:

• Design, develop, and maintain scalable data pipelines and ETL/ELT processes to support analytics and reporting requirements.
• Build and manage cloud-based data solutions using platforms such as AWS, Microsoft Fabric, or Databricks.
• Integrate data from multiple internal and external sources into centralized data platforms, data lakes, or data warehouses.
• Develop and maintain data models, data marts, and data warehouse structures to support business intelligence and analytics initiatives.
• Ensure data quality, integrity, and reliability through validation, monitoring, and automated quality checks.
• Work with large datasets and big data technologies such as Spark to process and transform complex data efficiently.
• Collaborate closely with data analysts, BI developers, data scientists, and business stakeholders to understand data requirements and deliver scalable solutions.

Qualifications and experience:

• Bachelor’s degree from an accredited university. A degree in computer science, engineering, information technology, or a related field is advantageous.
• Minimum of 7+ years of hands-on experience in data engineering or data platform development. Experience within the financial services industry would be beneficial but is not essential.
• Proven experience building, managing, and optimizing data pipelines and data solutions using on-premise or cloud-based technologies.
• Practical experience working with AWS services such as Glue, Redshift, S3, Lambda, EMR, and Athena, or exposure to Microsoft Fabric or Databricks.
• Familiarity with big data frameworks and technologies such as Apache Spark, Hadoop, or Kafka.
• Strong programming capabilities in Python, Pandas, and SQL for data processing and transformation