Data Engineer, Johannesburg

The Intermediate Data Engineer will be working within a growing data team in a dynamic telecommunications fibre environment. Working closely with the Data Specialist and senior members of the data team, the incumbent will design, build, and maintain scalable, efficient data pipelines and data platform artefacts that support business operations, network performance monitoring, customer analytics, and strategic decision-making. The ideal candidate will have solid hands-on experience with modern data engineering tooling, particularly Databricks and Python, and will demonstrate a practical understanding of data integration, data quality, and data architecture principles. They will be a self-sufficient contributor who can engage directly with data analysts and data owners to resolve data quality issues and translate business requirements into reliable, high-quality data solutions.

ACCOUNTABILITIES & RESPONSIBILITIES (KEY PERFORMANCE AREAS & TASKS)

• Design, build, and maintain scalable and reliable data pipelines using Databricks and Python, sourcing and transforming data from multiple internal and external systems.
• Develop and maintain ETL/ELT workflows and data transformation logic using Informatica / IDMC to automate the movement and processing of data across the platform.
• Build and maintain data lake and data warehouse artefacts, including tables, schemas, and data models, ensuring they are optimized for performance and scalability.
• Monitor data pipeline health and performance using Grafana dashboards; proactively identify, diagnose, and resolve pipeline failures and data anomalies.
• Implement and maintain data quality checks to ensure the accuracy, completeness, and consistency of data across all platforms.
• Consult data analysts and data owners to understand and resolve data quality issues, translating business requirements into practical data engineering solutions.
• Collaborate with the Data Specialist and business stakeholders to design data solutions that meet reporting, analytics, and operational needs.
• Support integration of structured and unstructured data from internal and external sources, ensuring data is accessible and fit for purpose.
• Apply data governance, security, and compliance standards in line with POPIA and relevant telecom regulatory requirements.
• Document data pipelines, transformation logic, data models, and architectural decisions to maintain an accurate and up-to-date knowledge base.
• Stay current with emerging data engineering tools, technologies, and best practices, and contribute to continuous improvement of the data platform.