This is a remote position. Our client, an organisation operating in the HealthTech sector, is seeking an experienced Data Engineer to support the development of scalable data solutions, robust data pipelines and Azure cloud infrastructure. The successful candidate will be responsible for integrating data from multiple sources, maintaining and optimising the enterprise data lake, and ensuring that data quality, governance, security and compliance standards are consistently upheld. Working closely with technical teams, business stakeholders and decision-makers, the Data Engineer will translate organisational requirements into reliable data engineering solutions that enable effective, secure and data-driven decision-making. Key Responsibilities - Design, develop and maintain scalable data pipelines and cloud-based data solutions. - Build and manage data ingestion, transformation, storage and integration processes. - Support, maintain and optimise the organisation’s enterprise data lake. - Contribute to the development and improvement of the organisation’s data architecture. - Deploy, configure and manage relevant Microsoft Azure cloud services. - Integrate data from multiple internal and external systems. - Support API development and system integration initiatives. - Translate business and technical requirements into practical data engineering solutions. - Implement monitoring, testing and troubleshooting processes to ensure pipeline reliability and performance. - Maintain high standards of data quality, integrity, availability and accessibility. - Ensure compliance with data governance, information security and regulatory requirements. - Collaborate with stakeholders to deliver data solutions, reporting capabilities and actionable insights. - Identify opportunities to improve data engineering processes, automation and scalability. - Promote data engineering best practices, innovation and continuous improvement across the organisation. Qualifications - Bachelor’s degree in Science, Technology, Engineering, Mathematics, Computer Science, Information Systems or a related discipline. - Relevant Microsoft Azure Data Engineering or Databricks certification. Additional certifications will be advantageous, including: - Microsoft Azure Solutions Architect - Microsoft Azure AI or Machine Learning - Microsoft Azure Data or Analytics certifications - Databricks Data Engineer certification ### Requirements - A minimum of five years’ experience in data engineering or a closely related role. - Demonstrated experience designing and developing scalable data pipelines. - Strong practical experience working with Microsoft Azure data services and cloud infrastructure. - Experience developing, managing or supporting enterprise data lakes. - Experience with data modelling, data architecture and data integration. - Experience integrating structured and unstructured data from multiple systems. - Knowledge of APIs, system integration and data exchange processes. - Strong understanding of data governance, data quality, privacy and information security principles. - Experience optimising data solutions for performance, reliability and scalability. - Proficiency in relevant programming and querying languages, including SQL and Python. - Experience with Databricks, Azure Data Factory, Azure Data Lake Storage and related Azure services will be highly advantageous. - Experience working within healthcare, HealthTech or another regulated environment will be advantageous. Key Competencies Analytical and Conceptual Thinking - The ability to identify relationships between information or situations that may not initially appear connected, uncover underlying issues and apply conceptual, creative or inductive reasoning to complex problems. Strategic and Big-Picture Thinking - The ability to understand how individual data solutions contribute to broader organisational objectives, while balancing immediate technical requirements with long-term architecture, scalability and sustainability. Problem-Solving - The ability to investigate complex data and integration challenges, identify root causes and develop practical, reliable and commercially appropriate solutions. Stakeholder Engagement - The ability to communicate effectively with both technical and non-technical stakeholders, clarify requirements and translate business needs into suitable data engineering solutions. Quality and Governance Orientation - A strong commitment to data accuracy, security, governance, documentation and regulatory compliance. Collaboration - The ability to work effectively across multidisciplinary teams and contribute constructively to shared organisational outcomes. Innovation and Continuous Improvement - A proactive approach to identifying better tools, technologies and processes that improve the organisation’s data capability. ### Benefits - Provident fund - Medical Aid - Group Life - Growth